example of marketing research hypothesis

How to write a hypothesis for marketing experimentation

• Apr 11, 2021
• 5 minute read

Creating your strongest marketing hypothesis

The potential for your marketing improvement depends on the strength of your testing hypotheses.

But where are you getting your test ideas from? Have you been scouring competitor sites, or perhaps pulling from previous designs on your site? The web is full of ideas and you’re full of ideas – there is no shortage of inspiration, that’s for sure.

Coming up with something you  want  to test isn’t hard to do. Coming up with something you  should  test can be hard to do.

Hard – yes. Impossible? No. Which is good news, because if you can’t create hypotheses for things that should be tested, your test results won’t mean mean much, and you probably shouldn’t be spending your time testing.

Taking the time to write your hypotheses correctly will help you structure your ideas, get better results, and avoid wasting traffic on poor test designs.

With this post, we’re getting advanced with marketing hypotheses, showing you how to write and structure your hypotheses to gain both business results and marketing insights!

By the time you finish reading, you’ll be able to:

• Distinguish a solid hypothesis from a time-waster, and
• Structure your solid hypothesis to get results  and  insights

To make this whole experience a bit more tangible, let’s track a sample idea from…well…idea to hypothesis.

Let’s say you identified a call-to-action (CTA)* while browsing the web, and you were inspired to test something similar on your own lead generation landing page. You think it might work for your users! Your idea is:

“My page needs a new CTA.”

*A call-to-action is the point where you, as a marketer, ask your prospect to do something on your page. It often includes a button or link to an action like “Buy”, “Sign up”, or “Request a quote”.

The basics: The correct marketing hypothesis format

A well-structured hypothesis provides insights whether it is proved, disproved, or results are inconclusive.

You should never phrase a marketing hypothesis as a question. It should be written as a statement that can be rejected or confirmed.

Further, it should be a statement geared toward revealing insights – with this in mind, it helps to imagine each statement followed by a  reason :

• Changing _______ into ______ will increase [conversion goal], because:
• Changing _______ into ______ will decrease [conversion goal], because:
• Changing _______ into ______ will not affect [conversion goal], because:

Each of the above sentences ends with ‘because’ to set the expectation that there will be an explanation behind the results of whatever you’re testing.

It’s important to remember to plan ahead when you create a test, and think about explaining why the test turned out the way it did when the results come in.

Level up: Moving from a good to great hypothesis

Understanding what makes an idea worth testing is necessary for your optimization team.

If your tests are based on random ideas you googled or were suggested by a consultant, your testing process still has its training wheels on. Great hypotheses aren’t random. They’re based on rationale and aim for learning.

Hypotheses should be based on themes and analysis that show potential conversion barriers.

At Conversion, we call this investigation phase the “Explore Phase” where we use frameworks like the LIFT Model to understand the prospect’s unique perspective. (You can read more on the the full optimization process here).

A well-founded marketing hypothesis should also provide you with new, testable clues about your users regardless of whether or not the test wins, loses or yields inconclusive results.

These new insights should inform future testing: a solid hypothesis can help you quickly separate worthwhile ideas from the rest when planning follow-up tests.

“Ultimately, what matters most is that you have a hypothesis going into each experiment and you design each experiment to address that hypothesis.” – Nick So, VP of Delivery

Here’s a quick tip :

If you’re about to run a test that isn’t going to tell you anything new about your users and their motivations, it’s probably not worth investing your time in.

Let’s take this opportunity to refer back to your original idea:

Ok, but  what now ? To get actionable insights from ‘a new CTA’, you need to know why it behaved the way it did. You need to ask the right question.

To test the waters, maybe you changed the copy of the CTA button on your lead generation form from “Submit” to “Send demo request”. If this change leads to an increase in conversions, it could mean that your users require more clarity about what their information is being used for.

That’s a potential insight.

Based on this insight, you could follow up with another test that adds copy around the CTA about next steps: what the user should anticipate after they have submitted their information.

For example, will they be speaking to a specialist via email? Will something be waiting for them the next time they visit your site? You can test providing more information, and see if your users are interested in knowing it!

That’s the cool thing about a good hypothesis: the results of the test, while important (of course) aren’t the only component driving your future test ideas. The insights gleaned lead to further hypotheses and insights in a virtuous cycle.

It’s based on a science

The term “hypothesis” probably isn’t foreign to you. In fact, it may bring up memories of grade-school science class; it’s a critical part of the  scientific method .

The scientific method in testing follows a systematic routine that sets ideation up to predict the results of experiments via:

• Collecting data and information through observation
• Creating tentative descriptions of what is being observed
• Forming  hypotheses  that predict different outcomes based on these observations
• Testing your  hypotheses
• Analyzing the data, drawing conclusions and insights from the results

Don’t worry! Hypothesizing may seem ‘sciency’, but it doesn’t have to be complicated in practice.

Hypothesizing simply helps ensure the results from your tests are quantifiable, and is necessary if you want to understand how the results reflect the change made in your test.

A strong marketing hypothesis allows testers to use a structured approach in order to discover what works, why it works, how it works, where it works, and who it works on.

“My page needs a new CTA.” Is this idea in its current state clear enough to help you understand what works? Maybe. Why it works? No. Where it works? Maybe. Who it works on? No.

Your idea needs refining.

Let’s pull back and take a broader look at the lead generation landing page we want to test.

Imagine the situation: you’ve been diligent in your data collection and you notice several recurrences of Clarity pain points – meaning that there are many unclear instances throughout the page’s messaging.

Rather than focusing on the CTA right off the bat, it may be more beneficial to deal with the bigger clarity issue.

Now you’re starting to think about solving your prospects conversion barriers rather than just testing random ideas!

If you believe the overall page is unclear, your overarching theme of inquiry might be positioned as:

• “Improving the clarity of the page will reduce confusion and improve [conversion goal].”

By testing a hypothesis that supports this clarity theme, you can gain confidence in the validity of it as an actionable marketing insight over time.

If the test results are negative : It may not be worth investigating this motivational barrier any further on this page. In this case, you could return to the data and look at the other motivational barriers that might be affecting user behavior.

If the test results are positive : You might want to continue to refine the clarity of the page’s message with further testing.

Typically, a test will start with a broad idea — you identify the changes to make, predict how those changes will impact your conversion goal, and write it out as a broad theme as shown above. Then, repeated tests aimed at that theme will confirm or undermine the strength of the underlying insight.

Building marketing hypotheses to create insights

You believe you’ve identified an overall problem on your landing page (there’s a problem with clarity). Now you want to understand how individual elements contribute to the problem, and the effect these individual elements have on your users.

It’s game time  – now you can start designing a hypothesis that will generate insights.

You believe your users need more clarity. You’re ready to dig deeper to find out if that’s true!

If a specific question needs answering, you should structure your test to make a single change. This isolation might ask: “What element are users most sensitive to when it comes to the lack of clarity?” and “What changes do I believe will support increasing clarity?”

At this point, you’ll want to boil down your overarching theme…

• Improving the clarity of the page will reduce confusion and improve [conversion goal].

…into a quantifiable hypothesis that isolates key sections:

• Changing the wording of this CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion about the next steps in the funnel and improve order completions.

Does this answer what works? Yes: changing the wording on your CTA.

Does this answer why it works? Yes: reducing confusion about the next steps in the funnel.

Does this answer where it works? Yes: on this page, before the user enters this theoretical funnel.

Does this answer who it works on? No, this question demands another isolation. You might structure your hypothesis more like this:

• Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion  for visitors coming from my email campaign  about the next steps in the funnel and improve order completions.

Now we’ve got a clear hypothesis. And one worth testing!

What makes a great hypothesis?

1. It’s testable.

2. It addresses conversion barriers.

3. It aims at gaining marketing insights.

Let’s compare:

The original idea : “My page needs a new CTA.”

Following the hypothesis structure : “A new CTA on my page will increase [conversion goal]”

The first test implied a problem with clarity, provides a potential theme : “Improving the clarity of the page will reduce confusion and improve [conversion goal].”

The potential clarity theme leads to a new hypothesis : “Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion about the next steps in the funnel and improve order completions.”

Final refined hypothesis : “Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion for visitors coming from my email campaign about the next steps in the funnel and improve order completions.”

Which test would you rather your team invest in?

Before you start your next test, take the time to do a proper analysis of the page you want to focus on. Do preliminary testing to define bigger issues, and use that information to refine and pinpoint your marketing hypothesis to give you forward-looking insights.

Doing this will help you avoid time-wasting tests, and enable you to start getting some insights for your team to keep testing!

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Expert Advice on Developing a Hypothesis for Marketing Experimentation 

• Conversion Rate Optimization

Simbar Dube

Every marketing experimentation process has to have a solid hypothesis. 

That’s a must – unless you want to be roaming in the dark and heading towards a dead-end in your experimentation program.

Hypothesizing is the second phase of our SHIP optimization process here at Invesp.

It comes after we have completed the research phase. 

This is an indication that we don’t just pull a hypothesis out of thin air. We always make sure that it is based on research data. 

But having a research-backed hypothesis doesn’t mean that the hypothesis will always be correct. In fact, tons of hypotheses bear inconclusive results or get disproved. 

The main idea of having a hypothesis in marketing experimentation is to help you gain insights – regardless of the testing outcome. 

By the time you finish reading this article, you’ll know: 

• The essential tips on what to do when crafting a hypothesis for marketing experiments
• How a marketing experiment hypothesis works 

How experts develop a solid hypothesis

The basics: marketing experimentation hypothesis.

A hypothesis is a research-based statement that aims to explain an observed trend and create a solution that will improve the result. This statement is an educated, testable prediction about what will happen.

It has to be stated in declarative form and not as a question.

“ If we add magnification info, product video and making virtual mirror buttons, will that improve engagement? ” is not declarative, but “ Improving the experience of product pages by adding magnification info, product video and making virtual mirror buttons will increase engagement ” is.

Here’s a quick example of how a hypothesis should be phrased: 

• Replacing ___ with __ will increase [conversion goal] by [%], because:
• Removing ___ and __ will decrease [conversion goal] by [%], because:
• Changing ___ into __ will not affect [conversion goal], because:
• Improving  ___ by  ___will increase [conversion goal], because: 

As you can see from the above sentences, a good hypothesis is written in clear and simple language. Reading your hypothesis should tell your team members exactly what you thought was going to happen in an experiment.

Another important element of a good hypothesis is that it defines the variables in easy-to-measure terms, like who the participants are, what changes during the testing, and what the effect of the changes will be: 

Example : Let’s say this is our hypothesis: 

Displaying full look items on every “continue shopping & view your bag” pop-up and highlighting the value of having a full look will improve the visibility of a full look, encourage visitors to add multiple items from the same look and that will increase the average order value, quantity with cross-selling by 3% .

Who are the participants : 

Visitors. 

What changes during the testing : 

Displaying full look items on every “continue shopping & view your bag” pop-up and highlighting the value of having a full look…

What the effect of the changes will be:  

Will improve the visibility of a full look, encourage visitors to add multiple items from the same look and that will increase the average order value, quantity with cross-selling by 3% .

Don’t bite off more than you can chew! Answering some scientific questions can involve more than one experiment, each with its own hypothesis. so, you have to make sure your hypothesis is a specific statement relating to a single experiment.

How a Marketing Experimentation Hypothesis Works

Assuming that you have done conversion research and you have identified a list of issues ( UX or conversion-related problems) and potential revenue opportunities on the site. The next thing you’d want to do is to prioritize the issues and determine which issues will most impact the bottom line.

Having ranked the issues you need to test them to determine which solution works best. At this point, you don’t have a clear solution for the problems identified. So, to get better results and avoid wasting traffic on poor test designs, you need to make sure that your testing plan is guided. 

This is where a hypothesis comes into play. 

For each and every problem you’re aiming to address, you need to craft a hypothesis for it – unless the problem is a technical issue that can be solved right away without the need to hypothesize or test. 

One important thing you should note about an experimentation hypothesis is that it can be implemented in different ways.  

This means that one hypothesis can have four or five different tests as illustrated in the image above. Khalid Saleh , the Invesp CEO, explains: 

“There are several ways that can be used to support one single hypothesis. Each and every way is a possible test scenario. And that means you also have to prioritize the test design you want to start with. Ultimately the name of the game is you want to find the idea that has the biggest possible impact on the bottom line with the least amount of effort. We use almost 18 different metrics to score all of those.”

In one of the recent tests we launched after watching video recordings, viewing heatmaps, and conducting expert reviews, we noticed that:  

• Visitors were scrolling to the bottom of the page to fill out a calculator so as to get a free diet plan. 
• Brand is missing 
• Too many free diet plans – and this made it hard for visitors to choose and understand.  
• No value proposition on the page
• The copy didn’t mention the benefits of the paid program
• There was no clear CTA for the next action

To help you understand, let’s have a look at how the original page looked like before we worked on it: 

So our aim was to make the shopping experience seamless for visitors, make the page more appealing and not confusing. In order to do that, here is how we phrased the hypothesis for the page above: 

Improving the experience of optin landing pages by making the free offer accessible above the fold and highlighting the next action with a clear CTA and will increase the engagement on the offer and increase the conversion rate by 1%.

For this particular hypothesis, we had two design variations aligned to it:

The two above designs are different, but they are aligned to one hypothesis. This goes on to show how one hypothesis can be implemented in different ways. Looking at the two variations above – which one do you think won?

Yes, you’re right, V2 was the winner. 

Considering that there are many ways you can implement one hypothesis, so when you launch a test and it fails, it doesn’t necessarily mean that the hypothesis was wrong. Khalid adds:

“A single failure of a test doesn’t mean that the hypothesis is incorrect. Nine times out of ten it’s because of the way you’ve implemented the hypothesis. Look at the way you’ve coded and look at the copy you’ve used – you are more likely going to find something wrong with it. Always be open.” 

So there are three things you should keep in mind when it comes to marketing experimentation hypotheses: 

• It takes a while for this hypothesis to really fully test it.
• A single failure doesn’t necessarily mean that the hypothesis is incorrect.
• Whether a hypothesis is proved or disproved, you can still learn something about your users.

I know it’s never easy to develop a hypothesis that informs future testing – I mean it takes a lot of intense research behind the scenes, and tons of ideas to begin with. So, I reached out to six CRO experts for tips and advice to help you understand more about developing a solid hypothesis and what to include in it. 

Maurice   says that a solid hypothesis should have not more than one goal: 

Maurice Beerthuyzen – CRO/CXO Lead at ClickValue “Creating a hypothesis doesn’t begin at the hypothesis itself. It starts with research. What do you notice in your data, customer surveys, and other sources? Do you understand what happens on your website? When you notice an opportunity it is tempting to base one single A/B test on one hypothesis. Create hypothesis A and run a single test, and then move forward to the next test. With another hypothesis. But it is very rare that you solve your problem with only one hypothesis. Often a test provides several other questions. Questions which you can solve with running other tests. But based on that same hypothesis! We should not come up with a new hypothesis for every test. Another mistake that often happens is that we fill the hypothesis with multiple goals. Then we expect that the hypothesis will work on conversion rate, average order value, and/or Click Through Ratio. Of course, this is possible, but when you run your test, your hypothesis can only have one goal at once. And what if you have two goals? Just split the hypothesis then create a secondary hypothesis for your second goal. Every test has one primary goal. What if you find a winner on your secondary hypothesis? Rerun the test with the second hypothesis as the primary one.”

Jon believes that a strong hypothesis is built upon three pillars:

Jon MacDonald – President and Founder of The Good Respond to an established challenge – The challenge must have a strong background based on data, and the background should state an established challenge that the test is looking to address. Example: “Sign up form lacks proof of value, incorrectly assuming if users are on the page, they already want the product.” Propose a specific solution – What is the one, the single thing that is believed will address the stated challenge? Example: “Adding an image of the dashboard as a background to the signup form…”. State the assumed impact – The assumed impact should reference one specific, measurable optimization goal that was established prior to forming a hypothesis. Example: “…will increase signups.” So, if your hypothesis doesn’t have a specific, measurable goal like “will increase signups,” you’re not really stating a test hypothesis!”

Matt uses his own hypothesis builder to collate important data points into a single hypothesis. 

Matt Beischel – Founder of Corvus CRO Like Jon, Matt also breaks down his hypothesis writing process into three sections. Unlike Jon, Matt sections are: Comprehension Response Outcome I set it up so that the names neatly match the “CRO.” It’s a sort of “mad-libs” style fill-in-the-blank where each input is an important piece of information for building out a robust hypothesis. I consider these the minimum required data points for a good hypothesis; if you can’t completely fill out the form, then you don’t have a good hypothesis. Here’s a breakdown of each data point: Comprehension – Identifying something that can be improved upon Problem: “What is a problem we have?” Observation Method: “How did we identify the problem?” Response – Change that can cause improvement Variation: “What change do we think could solve the problem?” Location: “Where should the change occur?” Scope: “What are the conditions for the change?” Audience: “Who should the change affect?” Outcome – Measurable result of the change that determines the success Behavior Change : “What change in behavior are we trying to affect?” Primary KPI: “What is the important metric that determines business impact?” Secondary KPIs: “Other metrics that will help reinforce/refute the Primary KPI” Something else to consider is that I have a “user first” approach to formulating hypotheses. My process above is always considered within the context of how it would first benefit the user. Now, I do feel that a successful experiment should satisfy the needs of BOTH users and businesses, but always be in favor of the user. Notice that “Behavior Change” is the first thing listed in Outcome, not primary business KPI. Sure, at the end of the day you are working for the business’s best interests (both strategically and financially), but placing the user first will better inform your decision making and prioritization; there’s a reason that things like personas, user stories, surveys, session replays, reviews, etc. exist after all. A business-first ideology is how you end up with dark patterns and damaging brand credibility.”

One of the many mistakes that CROs make when writing a hypothesis is that they are focused on wins and not on insights. Shiva advises against this mindset:

Shiva Manjunath – Marketing Manager and CRO at Gartner “Test to learn, not test to win. It’s a very simple reframe of hypotheses but can have a magnitude of difference. Here’s an example: Test to Win Hypothesis: If I put a product video in the middle of the product page, I will improve add to cart rates and improve CVR. Test to Learn Hypothesis: If I put a product video on the product page, there will be high engagement with the video and it will positively influence traffic What you’re doing is framing your hypothesis, and test, in a particular way to learn as much as you can. That is where you gain marketing insights. The more you run ‘marketing insight’ tests, the more you will win. Why? The more you compound marketing insight learnings, your win velocity will start to increase as a proxy of the learnings you’ve achieved. Then, you’ll have a higher chance of winning in your tests – and the more you’ll be able to drive business results.”

Lorenzo  says it’s okay to focus on achieving a certain result as long as you are also getting an answer to: “Why is this event happening or not happening?”

Lorenzo Carreri – CRO Consultant “When I come up with a hypothesis for a new or iterative experiment, I always try to find an answer to a question. It could be something related to a problem people have or an opportunity to achieve a result or a way to learn something. The main question I want to answer is “Why is this event happening or not happening?” The question is driven by data, both qualitative and quantitative. The structure I use for stating my hypothesis is: From [data source], I noticed [this problem/opportunity] among [this audience of users] on [this page or multiple pages]. So I believe that by [offering this experiment solution], [this KPI] will [increase/decrease/stay the same].

Jakub Linowski says that hypotheses are meant to hold researchers accountable:

Jakub Linowski – Chief Editor of GoodUI “They do this by making your change and prediction more explicit. A typical hypothesis may be expressed as: If we change (X), then it will have some measurable effect (A). Unfortunately, this oversimplified format can also become a heavy burden to your experiment design with its extreme reductionism. However you decide to format your hypotheses, here are three suggestions for more flexibility to avoid limiting yourself. One Or More Changes To break out of the first limitation, we have to admit that our experiments may contain a single or multiple changes. Whereas the classic hypothesis encourages a single change or isolated variable, it’s not the only way we can run experiments. In the real world, it’s quite normal to see multiple design changes inside a single variation. One valid reason for doing this is when wishing to optimize a section of a website while aiming for a greater effect. As more positive changes compound together, there are times when teams decide to run bigger experiments. An experiment design (along with your hypotheses) therefore should allow for both single or multiple changes. One Or More Metrics A second limitation of many hypotheses is that they often ask us to only make a single prediction at a time. There are times when we might like to make multiple guesses or predictions to a set of metrics. A simple example of this might be a trade-off experiment with a guess of increased sales but decreased trial signups. Being able to express single or multiple metrics in our experimental designs should therefore be possible. Estimates, Directional Predictions, Or Unknowns Finally, traditional hypotheses also tend to force very simple directional predictions by asking us to guess whether something will increase or decrease. In reality, however, the fidelity of predictions can be higher or lower. On one hand, I’ve seen and made experiment estimations that contain specific numbers from prior data (ex: increase sales by 14%). While at other times it should also be acceptable to admit the unknown and leave the prediction blank. One example of this is when we are testing a completely novel idea without any prior data in a highly exploratory type of experiment. In such cases, it might be dishonest to make any sort of predictions and we should allow ourselves to express the unknown comfortably.”

Conclusion 

So there you have it! Before you jump on launching a test, start by making sure that your hypothesis is solid and backed by research. Ask yourself the questions below when crafting a hypothesis for marketing experimentation:

• Is the hypothesis backed by research?
• Can the hypothesis be tested?
• Does the hypothesis provide insights?
• Does the hypothesis set the expectation that there will be an explanation behind the results of whatever you’re testing?

Don’t worry! Hypothesizing may seem like a very complicated process, but it’s not complicated in practice especially when you have done proper research.

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A/B Testing in Digital Marketing: Example of four-step hypothesis framework

by Daniel Burstein , Senior Director, Content & Marketing, MarketingSherpa and MECLABS Institute

This article was originally published in the MarketingSherpa email newsletter .

If you are a marketing expert — whether in a brand’s marketing department or at an advertising agency — you may feel the need to be absolutely sure in an unsure world.

What should the headline be? What images should we use? Is this strategy correct? Will customers value this promo?

This is the stuff you’re paid to know. So you may feel like you must boldly proclaim your confident opinion.

But you can’t predict the future with 100% accuracy. You can’t know with absolute certainty how humans will behave. And let’s face it, even as marketing experts we’re occasionally wrong.

It’s not bad, it’s healthy. And the most effective way to overcome that doubt is by testing our marketing creative to see what really works.

Developing a hypothesis

After we published Value Sequencing: A step-by-step examination of a landing page that generated 638% more conversions , a MarketingSherpa reader emailed us and asked …

Great stuff Daniel. Much appreciated. I can see you addressing all the issues there.

I thought I saw one more opportunity to expand on what you made. Would you consider adding the IF, BY, WILL, BECAUSE to the control/treatment sections so we can see what psychology you were addressing so we know how to create the hypothesis to learn from what the customer is currently doing and why and then form a test to address that? The video today on customer theory was great (Editor’s Note: Part of the MarketingExperiments YouTube Live series ) . I think there is a way to incorporate that customer theory thinking into this article to take it even further.

Developing a hypothesis is an essential part of marketing experimentation. Qualitative-based research should inform hypotheses that you test with real-world behavior.

The hypotheses help you discover how accurate those insights from qualitative research are. If you engage in hypothesis-driven testing, then you ensure your tests are strategic (not just based on a random idea) and built in a way that enables you to learn more and more about the customer with each test.

And that methodology will ultimately lead to greater and greater lifts over time, instead of a scattershot approach where sometimes you get a lift and sometimes you don’t, but you never really know why.

Here is a handy tool to help you in developing hypotheses — the MECLABS Four-Step Hypothesis Framework.

As the reader suggests, I will use the landing page test referenced in the previous article as an example. ( Please note: While the experiment in that article was created with a hypothesis-driven approach, this specific four-step framework is fairly new and was not in common use by the MECLABS team at that time, so I have created this specific example after the test was developed based on what I see in the test).

Here is what the hypothesis would look like for that test, and then we’ll break down each part individually:

If we emphasize the process-level value by adding headlines, images and body copy, we will generate more leads because the value of a longer landing page in reducing the anxiety of calling a TeleAgent outweighs the additional friction of a longer page.

IF: Summary description

The hypothesis begins with an overall statement about what you are trying to do in the experiment. In this case, the experiment is trying to emphasize the process-level value proposition (one of the four essential levels of value proposition ) of having a phone call with a TeleAgent.

The control landing page was emphasizing the primary value proposition of the brand itself.

The treatment landing page is essentially trying to answer this value proposition question: If I am your ideal customer, why should I call a TeleAgent rather than take any other action to learn more about my Medicare options?

The control landing page was asking a much bigger question that customers weren’t ready to say “yes” to yet, and it was overlooking the anxiety inherent in getting on a phone call with someone who might try to sell you something: If I am your ideal customer, why should I buy from your company instead of any other company.

This step answers WHAT you are trying to do.

BY: Remove, add, change

The next step answers HOW you are going to do it.

As Flint McGlaughlin, CEO and Managing Director of MECLABS Institute teaches, there are only three ways to improve performance: removing, adding or changing .

In this case, the team focused mostly on adding — adding headlines, images and body copy that highlighted the TeleAgents as trusted advisors.

“Adding” can be counterintuitive for many marketers. The team’s original landing page was short. Conventional wisdom says customers won’t read long landing pages. When I’m presenting to a group of marketers, I’ll put a short and long landing page on a slide and ask which page they think achieved better results.

Invariably I will hear, “Oh, the shorter page. I would never read something that long.”

That first-person statement is a mistake. Your marketing creative should not be based on “I” — the marketer. It should be based on “they” — the customer.

Most importantly, you need to focus on the customer at a specific point in time — when he or she is in the mindspace of considering to take an action like purchase a product or in need of more information before they decide to download a whitepaper. And sometimes in these situations, longer landing pages perform better.

In the case of this landing page, even the customer may not necessarily favor a long landing page all the time. But in the real-world situation when they are considering whether to call a TeleAgent or not, the added value helps more customers decide to take the action.

WILL: Improve performance

This is your KPI (key performance indicator). This step answers another HOW question: How do you know your hypothesis has been supported or refuted?

You can choose secondary metrics to monitor during your test as well. This might help you interpret the customer behavior observed in the test.

But ultimately, the hypothesis should rest on a single metric.

For this test, the goal was to generate more leads. And the treatment did — 638% more leads.

BECAUSE: Customer insight

This last step answers a WHY question — why did the customers act this way?

This helps you determine what you can learn about customers based on the actions observed in the experiment.

This is ultimately why you test. To learn about the customer and continually refine your company’s customer theory .

In this case, the team theorized that the value of a longer landing page in reducing the anxiety of calling a TeleAgent outweighs the additional friction of a longer landing page.

And the test results support that hypothesis.

Related Resources

The Hypothesis and the Modern-Day Marketer

Boost your Conversion Rate with a MECLABS Quick Win Intensive

Designing Hypotheses that Win: A four-step framework for gaining customer wisdom and generating marketing results

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 19 August 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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10.2 Steps in the Marketing Research Process

Learning objective.

• Describe the basic steps in the marketing research process and the purpose of each step.

The basic steps used to conduct marketing research are shown in Figure 10.6 “Steps in the Marketing Research Process” . Next, we discuss each step.

Figure 10.6 Steps in the Marketing Research Process

Step 1: Define the Problem (or Opportunity)

There’s a saying in marketing research that a problem half defined is a problem half solved. Defining the “problem” of the research sounds simple, doesn’t it? Suppose your product is tutoring other students in a subject you’re a whiz at. You have been tutoring for a while, and people have begun to realize you’re darned good at it. Then, suddenly, your business drops off. Or it explodes, and you can’t cope with the number of students you’re being asked help. If the business has exploded, should you try to expand your services? Perhaps you should subcontract with some other “whiz” students. You would send them students to be tutored, and they would give you a cut of their pay for each student you referred to them.

Both of these scenarios would be a problem for you, wouldn’t they? They are problems insofar as they cause you headaches. But are they really the problem? Or are they the symptoms of something bigger? For example, maybe your business has dropped off because your school is experiencing financial trouble and has lowered the number of scholarships given to incoming freshmen. Consequently, there are fewer total students on campus who need your services. Conversely, if you’re swamped with people who want you to tutor them, perhaps your school awarded more scholarships than usual, so there are a greater number of students who need your services. Alternately, perhaps you ran an ad in your school’s college newspaper, and that led to the influx of students wanting you to tutor them.

Businesses are in the same boat you are as a tutor. They take a look at symptoms and try to drill down to the potential causes. If you approach a marketing research company with either scenario—either too much or too little business—the firm will seek more information from you such as the following:

• In what semester(s) did your tutoring revenues fall (or rise)?
• In what subject areas did your tutoring revenues fall (or rise)?
• In what sales channels did revenues fall (or rise): Were there fewer (or more) referrals from professors or other students? Did the ad you ran result in fewer (or more) referrals this month than in the past months?
• Among what demographic groups did your revenues fall (or rise)—women or men, people with certain majors, or first-year, second-, third-, or fourth-year students?

The key is to look at all potential causes so as to narrow the parameters of the study to the information you actually need to make a good decision about how to fix your business if revenues have dropped or whether or not to expand it if your revenues have exploded.

The next task for the researcher is to put into writing the research objective. The research objective is the goal(s) the research is supposed to accomplish. The marketing research objective for your tutoring business might read as follows:

To survey college professors who teach 100- and 200-level math courses to determine why the number of students referred for tutoring dropped in the second semester.

This is admittedly a simple example designed to help you understand the basic concept. If you take a marketing research course, you will learn that research objectives get a lot more complicated than this. The following is an example:

“To gather information from a sample representative of the U.S. population among those who are ‘very likely’ to purchase an automobile within the next 6 months, which assesses preferences (measured on a 1–5 scale ranging from ‘very likely to buy’ to ‘not likely at all to buy’) for the model diesel at three different price levels. Such data would serve as input into a forecasting model that would forecast unit sales, by geographic regions of the country, for each combination of the model’s different prices and fuel configurations (Burns & Bush, 2010).”

Now do you understand why defining the problem is complicated and half the battle? Many a marketing research effort is doomed from the start because the problem was improperly defined. Coke’s ill-fated decision to change the formula of Coca-Cola in 1985 is a case in point: Pepsi had been creeping up on Coke in terms of market share over the years as well as running a successful promotional campaign called the “Pepsi Challenge,” in which consumers were encouraged to do a blind taste test to see if they agreed that Pepsi was better. Coke spent four years researching “the problem.” Indeed, people seemed to like the taste of Pepsi better in blind taste tests. Thus, the formula for Coke was changed. But the outcry among the public was so great that the new formula didn’t last long—a matter of months—before the old formula was reinstated. Some marketing experts believe Coke incorrectly defined the problem as “How can we beat Pepsi in taste tests?” instead of “How can we gain market share against Pepsi?” (Burns & Bush, 2010)

New Coke Is It! 1985

(click to see video)

This video documents the Coca-Cola Company’s ill-fated launch of New Coke in 1985.

1985 Pepsi Commercial—“They Changed My Coke”

This video shows how Pepsi tried to capitalize on the blunder.

Step 2: Design the Research

The next step in the marketing research process is to do a research design. The research design is your “plan of attack.” It outlines what data you are going to gather and from whom, how and when you will collect the data, and how you will analyze it once it’s been obtained. Let’s look at the data you’re going to gather first.

There are two basic types of data you can gather. The first is primary data. Primary data is information you collect yourself, using hands-on tools such as interviews or surveys, specifically for the research project you’re conducting. Secondary data is data that has already been collected by someone else, or data you have already collected for another purpose. Collecting primary data is more time consuming, work intensive, and expensive than collecting secondary data. Consequently, you should always try to collect secondary data first to solve your research problem, if you can. A great deal of research on a wide variety of topics already exists. If this research contains the answer to your question, there is no need for you to replicate it. Why reinvent the wheel?

Sources of Secondary Data

Your company’s internal records are a source of secondary data. So are any data you collect as part of your marketing intelligence gathering efforts. You can also purchase syndicated research. Syndicated research is primary data that marketing research firms collect on a regular basis and sell to other companies. J.D. Power & Associates is a provider of syndicated research. The company conducts independent, unbiased surveys of customer satisfaction, product quality, and buyer behavior for various industries. The company is best known for its research in the automobile sector. One of the best-known sellers of syndicated research is the Nielsen Company, which produces the Nielsen ratings. The Nielsen ratings measure the size of television, radio, and newspaper audiences in various markets. You have probably read or heard about TV shows that get the highest (Nielsen) ratings. (Arbitron does the same thing for radio ratings.) Nielsen, along with its main competitor, Information Resources, Inc. (IRI), also sells businesses scanner-based research . Scanner-based research is information collected by scanners at checkout stands in stores. Each week Nielsen and IRI collect information on the millions of purchases made at stores. The companies then compile the information and sell it to firms in various industries that subscribe to their services. The Nielsen Company has also recently teamed up with Facebook to collect marketing research information. Via Facebook, users will see surveys in some of the spaces in which they used to see online ads (Rappeport, Gelles, 2009).

By contrast, MarketResearch.com is an example of a marketing research aggregator. A marketing research aggregator is a marketing research company that doesn’t conduct its own research and sell it. Instead, it buys research reports from other marketing research companies and then sells the reports in their entirety or in pieces to other firms. Check out MarketResearch.com’s Web site. As you will see there are a huge number of studies in every category imaginable that you can buy for relatively small amounts of money.

Figure 10.7

Market research aggregators buy research reports from other marketing research companies and then resell them in part or in whole to other companies so they don’t have to gather primary data.

Source: http://www.marketresearch.com .

Your local library is a good place to gather free secondary data. It has searchable databases as well as handbooks, dictionaries, and books, some of which you can access online. Government agencies also collect and report information on demographics, economic and employment data, health information, and balance-of-trade statistics, among a lot of other information. The U.S. Census Bureau collects census data every ten years to gather information about who lives where. Basic demographic information about sex, age, race, and types of housing in which people live in each U.S. state, metropolitan area, and rural area is gathered so that population shifts can be tracked for various purposes, including determining the number of legislators each state should have in the U.S. House of Representatives. For the U.S. government, this is primary data. For marketing managers it is an important source of secondary data.

The Survey Research Center at the University of Michigan also conducts periodic surveys and publishes information about trends in the United States. One research study the center continually conducts is called the “Changing Lives of American Families” ( http://www.isr.umich.edu/home/news/research-update/2007-01.pdf ). This is important research data for marketing managers monitoring consumer trends in the marketplace. The World Bank and the United Nations are two international organizations that collect a great deal of information. Their Web sites contain many free research studies and data related to global markets. Table 10.1 “Examples of Primary Data Sources versus Secondary Data Sources” shows some examples of primary versus secondary data sources.

Table 10.1 Examples of Primary Data Sources versus Secondary Data Sources

Gauging the Quality of Secondary Data

When you are gathering secondary information, it’s always good to be a little skeptical of it. Sometimes studies are commissioned to produce the result a client wants to hear—or wants the public to hear. For example, throughout the twentieth century, numerous studies found that smoking was good for people’s health. The problem was the studies were commissioned by the tobacco industry. Web research can also pose certain hazards. There are many biased sites that try to fool people that they are providing good data. Often the data is favorable to the products they are trying to sell. Beware of product reviews as well. Unscrupulous sellers sometimes get online and create bogus ratings for products. See below for questions you can ask to help gauge the credibility of secondary information.

Gauging the Credibility of Secondary Data: Questions to Ask

• Who gathered this information?
• For what purpose?
• What does the person or organization that gathered the information have to gain by doing so?
• Was the information gathered and reported in a systematic manner?
• Is the source of the information accepted as an authority by other experts in the field?
• Does the article provide objective evidence to support the position presented?

Types of Research Design

Now let’s look specifically at the types of research designs that are utilized. By understanding different types of research designs, a researcher can solve a client’s problems more quickly and efficiently without jumping through more hoops than necessary. Research designs fall into one of the following three categories:

• Exploratory research design
• Descriptive research design
• Causal research design (experiments)

An exploratory research design is useful when you are initially investigating a problem but you haven’t defined it well enough to do an in-depth study of it. Perhaps via your regular market intelligence, you have spotted what appears to be a new opportunity in the marketplace. You would then do exploratory research to investigate it further and “get your feet wet,” as the saying goes. Exploratory research is less structured than other types of research, and secondary data is often utilized.

One form of exploratory research is qualitative research. Qualitative research is any form of research that includes gathering data that is not quantitative, and often involves exploring questions such as why as much as what or how much . Different forms, such as depth interviews and focus group interviews, are common in marketing research.

The depth interview —engaging in detailed, one-on-one, question-and-answer sessions with potential buyers—is an exploratory research technique. However, unlike surveys, the people being interviewed aren’t asked a series of standard questions. Instead the interviewer is armed with some general topics and asks questions that are open ended, meaning that they allow the interviewee to elaborate. “How did you feel about the product after you purchased it?” is an example of a question that might be asked. A depth interview also allows a researcher to ask logical follow-up questions such as “Can you tell me what you mean when you say you felt uncomfortable using the service?” or “Can you give me some examples?” to help dig further and shed additional light on the research problem. Depth interviews can be conducted in person or over the phone. The interviewer either takes notes or records the interview.

Focus groups and case studies are often utilized for exploratory research as well. A focus group is a group of potential buyers who are brought together to discuss a marketing research topic with one another. A moderator is used to focus the discussion, the sessions are recorded, and the main points of consensus are later summarized by the market researcher. Textbook publishers often gather groups of professors at educational conferences to participate in focus groups. However, focus groups can also be conducted on the telephone, in online chat rooms, or both, using meeting software like WebEx. The basic steps of conducting a focus group are outlined below.

The Basic Steps of Conducting a Focus Group

• Establish the objectives of the focus group. What is its purpose?
• Identify the people who will participate in the focus group. What makes them qualified to participate? How many of them will you need and what they will be paid?
• Obtain contact information for the participants and send out invitations (usually e-mails are most efficient).
• Develop a list of questions.
• Choose a facilitator.
• Choose a location in which to hold the focus group and the method by which it will be recorded.
• Conduct the focus group. If the focus group is not conducted electronically, include name tags for the participants, pens and notepads, any materials the participants need to see, and refreshments. Record participants’ responses.
• Summarize the notes from the focus group and write a report for management.

A case study looks at how another company solved the problem that’s being researched. Sometimes multiple cases, or companies, are used in a study. Case studies nonetheless have a mixed reputation. Some researchers believe it’s hard to generalize, or apply, the results of a case study to other companies. Nonetheless, collecting information about companies that encountered the same problems your firm is facing can give you a certain amount of insight about what direction you should take. In fact, one way to begin a research project is to carefully study a successful product or service.

Two other types of qualitative data used for exploratory research are ethnographies and projective techniques. In an ethnography , researchers interview, observe, and often videotape people while they work, live, shop, and play. The Walt Disney Company has recently begun using ethnographers to uncover the likes and dislikes of boys aged six to fourteen, a financially attractive market segment for Disney, but one in which the company has been losing market share. The ethnographers visit the homes of boys, observe the things they have in their rooms to get a sense of their hobbies, and accompany them and their mothers when they shop to see where they go, what the boys are interested in, and what they ultimately buy. (The children get seventy-five dollars out of the deal, incidentally.) (Barnes, 2009)

Projective techniques are used to reveal information research respondents might not reveal by being asked directly. Asking a person to complete sentences such as the following is one technique:

People who buy Coach handbags __________.

(Will he or she reply with “are cool,” “are affluent,” or “are pretentious,” for example?)

KFC’s grilled chicken is ______.

Or the person might be asked to finish a story that presents a certain scenario. Word associations are also used to discern people’s underlying attitudes toward goods and services. Using a word-association technique, a market researcher asks a person to say or write the first word that comes to his or her mind in response to another word. If the initial word is “fast food,” what word does the person associate it with or respond with? Is it “McDonald’s”? If many people reply that way, and you’re conducting research for Burger King, that could indicate Burger King has a problem. However, if the research is being conducted for Wendy’s, which recently began running an advertising campaign to the effect that Wendy’s offerings are “better than fast food,” it could indicate that the campaign is working.

Completing cartoons is yet another type of projective technique. It’s similar to finishing a sentence or story, only with the pictures. People are asked to look at a cartoon such as the one shown in Figure 10.8 “Example of a Cartoon-Completion Projective Technique” . One of the characters in the picture will have made a statement, and the person is asked to fill in the empty cartoon “bubble” with how they think the second character will respond.

Figure 10.8 Example of a Cartoon-Completion Projective Technique

In some cases, your research might end with exploratory research. Perhaps you have discovered your organization lacks the resources needed to produce the product. In other cases, you might decide you need more in-depth, quantitative research such as descriptive research or causal research, which are discussed next. Most marketing research professionals advise using both types of research, if it’s feasible. On the one hand, the qualitative-type research used in exploratory research is often considered too “lightweight.” Remember earlier in the chapter when we discussed telephone answering machines and the hit TV sitcom Seinfeld ? Both product ideas were initially rejected by focus groups. On the other hand, relying solely on quantitative information often results in market research that lacks ideas.

The Stone Wheel—What One Focus Group Said

Watch the video to see a funny spoof on the usefulness—or lack of usefulness—of focus groups.

Descriptive Research

Anything that can be observed and counted falls into the category of descriptive research design. A study using a descriptive research design involves gathering hard numbers, often via surveys, to describe or measure a phenomenon so as to answer the questions of who , what , where , when , and how . “On a scale of 1–5, how satisfied were you with your service?” is a question that illustrates the information a descriptive research design is supposed to capture.

Physiological measurements also fall into the category of descriptive design. Physiological measurements measure people’s involuntary physical responses to marketing stimuli, such as an advertisement. Elsewhere, we explained that researchers have gone so far as to scan the brains of consumers to see what they really think about products versus what they say about them. Eye tracking is another cutting-edge type of physiological measurement. It involves recording the movements of a person’s eyes when they look at some sort of stimulus, such as a banner ad or a Web page. The Walt Disney Company has a research facility in Austin, Texas, that it uses to take physical measurements of viewers when they see Disney programs and advertisements. The facility measures three types of responses: people’s heart rates, skin changes, and eye movements (eye tracking) (Spangler, 2009).

Figure 10.9

A woman shows off her headgear for an eye-tracking study. The gear’s not exactly a fashion statement but . . .

lawrencegs – Google Glass – CC BY 2.0.

A strictly descriptive research design instrument—a survey, for example—can tell you how satisfied your customers are. It can’t, however, tell you why. Nor can an eye-tracking study tell you why people’s eyes tend to dwell on certain types of banner ads—only that they do. To answer “why” questions an exploratory research design or causal research design is needed (Wagner, 2007).

Causal Research

Causal research design examines cause-and-effect relationships. Using a causal research design allows researchers to answer “what if” types of questions. In other words, if a firm changes X (say, a product’s price, design, placement, or advertising), what will happen to Y (say, sales or customer loyalty)? To conduct causal research, the researcher designs an experiment that “controls,” or holds constant, all of a product’s marketing elements except one (or using advanced techniques of research, a few elements can be studied at the same time). The one variable is changed, and the effect is then measured. Sometimes the experiments are conducted in a laboratory using a simulated setting designed to replicate the conditions buyers would experience. Or the experiments may be conducted in a virtual computer setting.

You might think setting up an experiment in a virtual world such as the online game Second Life would be a viable way to conduct controlled marketing research. Some companies have tried to use Second Life for this purpose, but the results have been somewhat mixed as to whether or not it is a good medium for marketing research. The German marketing research firm Komjuniti was one of the first “real-world” companies to set up an “island” in Second Life upon which it could conduct marketing research. However, with so many other attractive fantasy islands in which to play, the company found it difficult to get Second Life residents, or players, to voluntarily visit the island and stay long enough so meaningful research could be conducted. (Plus, the “residents,” or players, in Second Life have been known to protest corporations invading their world. When the German firm Komjuniti created an island in Second Life to conduct marketing research, the residents showed up waving signs and threatening to boycott the island.) (Wagner, 2007)

Why is being able to control the setting so important? Let’s say you are an American flag manufacturer and you are working with Walmart to conduct an experiment to see where in its stores American flags should be placed so as to increase their sales. Then the terrorist attacks of 9/11 occur. In the days afterward, sales skyrocketed—people bought flags no matter where they were displayed. Obviously, the terrorist attacks in the United States would have skewed the experiment’s data.

An experiment conducted in a natural setting such as a store is referred to as a field experiment . Companies sometimes do field experiments either because it is more convenient or because they want to see if buyers will behave the same way in the “real world” as in a laboratory or on a computer. The place the experiment is conducted or the demographic group of people the experiment is administered to is considered the test market . Before a large company rolls out a product to the entire marketplace, it will often place the offering in a test market to see how well it will be received. For example, to compete with MillerCoors’ sixty-four-calorie beer MGD 64, Anheuser-Busch recently began testing its Select 55 beer in certain cities around the country (McWilliams, 2009).

Figure 10.10

Select 55 beer: Coming soon to a test market near you? (If you’re on a diet, you have to hope so!)

Martine – Le champagne – CC BY-NC 2.0.

Many companies use experiments to test all of their marketing communications. For example, the online discount retailer O.co (formerly called Overstock.com) carefully tests all of its marketing offers and tracks the results of each one. One study the company conducted combined twenty-six different variables related to offers e-mailed to several thousand customers. The study resulted in a decision to send a group of e-mails to different segments. The company then tracked the results of the sales generated to see if they were in line with the earlier experiment it had conducted that led it to make the offer.

Step 3: Design the Data-Collection Forms

If the behavior of buyers is being formally observed, and a number of different researchers are conducting observations, the data obviously need to be recorded on a standardized data-collection form that’s either paper or electronic. Otherwise, the data collected will not be comparable. The items on the form could include a shopper’s sex; his or her approximate age; whether the person seemed hurried, moderately hurried, or unhurried; and whether or not he or she read the label on products, used coupons, and so forth.

The same is true when it comes to surveying people with questionnaires. Surveying people is one of the most commonly used techniques to collect quantitative data. Surveys are popular because they can be easily administered to large numbers of people fairly quickly. However, to produce the best results, the questionnaire for the survey needs to be carefully designed.

Questionnaire Design

Most questionnaires follow a similar format: They begin with an introduction describing what the study is for, followed by instructions for completing the questionnaire and, if necessary, returning it to the market researcher. The first few questions that appear on the questionnaire are usually basic, warm-up type of questions the respondent can readily answer, such as the respondent’s age, level of education, place of residence, and so forth. The warm-up questions are then followed by a logical progression of more detailed, in-depth questions that get to the heart of the question being researched. Lastly, the questionnaire wraps up with a statement that thanks the respondent for participating in the survey and information and explains when and how they will be paid for participating. To see some examples of questionnaires and how they are laid out, click on the following link: http://cas.uah.edu/wrenb/mkt343/Project/Sample%20Questionnaires.htm .

How the questions themselves are worded is extremely important. It’s human nature for respondents to want to provide the “correct” answers to the person administering the survey, so as to seem agreeable. Therefore, there is always a hazard that people will try to tell you what you want to hear on a survey. Consequently, care needs to be taken that the survey questions are written in an unbiased, neutral way. In other words, they shouldn’t lead a person taking the questionnaire to answer a question one way or another by virtue of the way you have worded it. The following is an example of a leading question.

Don’t you agree that teachers should be paid more ?

The questions also need to be clear and unambiguous. Consider the following question:

Which brand of toothpaste do you use ?

The question sounds clear enough, but is it really? What if the respondent recently switched brands? What if she uses Crest at home, but while away from home or traveling, she uses Colgate’s Wisp portable toothpaste-and-brush product? How will the respondent answer the question? Rewording the question as follows so it’s more specific will help make the question clearer:

Which brand of toothpaste have you used at home in the past six months? If you have used more than one brand, please list each of them 1 .

Sensitive questions have to be asked carefully. For example, asking a respondent, “Do you consider yourself a light, moderate, or heavy drinker?” can be tricky. Few people want to admit to being heavy drinkers. You can “soften” the question by including a range of answers, as the following example shows:

How many alcoholic beverages do you consume in a week ?

• __0–5 alcoholic beverages
• __5–10 alcoholic beverages
• __10–15 alcoholic beverages

Many people don’t like to answer questions about their income levels. Asking them to specify income ranges rather than divulge their actual incomes can help.

Other research question “don’ts” include using jargon and acronyms that could confuse people. “How often do you IM?” is an example. Also, don’t muddy the waters by asking two questions in the same question, something researchers refer to as a double-barreled question . “Do you think parents should spend more time with their children and/or their teachers?” is an example of a double-barreled question.

Open-ended questions , or questions that ask respondents to elaborate, can be included. However, they are harder to tabulate than closed-ended questions , or questions that limit a respondent’s answers. Multiple-choice and yes-and-no questions are examples of closed-ended questions.

Testing the Questionnaire

You have probably heard the phrase “garbage in, garbage out.” If the questions are bad, the information gathered will be bad, too. One way to make sure you don’t end up with garbage is to test the questionnaire before sending it out to find out if there are any problems with it. Is there enough space for people to elaborate on open-ended questions? Is the font readable? To test the questionnaire, marketing research professionals first administer it to a number of respondents face to face. This gives the respondents the chance to ask the researcher about questions or instructions that are unclear or don’t make sense to them. The researcher then administers the questionnaire to a small subset of respondents in the actual way the survey is going to be disseminated, whether it’s delivered via phone, in person, by mail, or online.

Getting people to participate and complete questionnaires can be difficult. If the questionnaire is too long or hard to read, many people won’t complete it. So, by all means, eliminate any questions that aren’t necessary. Of course, including some sort of monetary incentive for completing the survey can increase the number of completed questionnaires a market researcher will receive.

Step 4: Specify the Sample

Once you have created your questionnaire or other marketing study, how do you figure out who should participate in it? Obviously, you can’t survey or observe all potential buyers in the marketplace. Instead, you must choose a sample. A sample is a subset of potential buyers that are representative of your entire target market, or population being studied. Sometimes market researchers refer to the population as the universe to reflect the fact that it includes the entire target market, whether it consists of a million people, a hundred thousand, a few hundred, or a dozen. “All unmarried people over the age of eighteen who purchased Dirt Devil steam cleaners in the United States during 2011” is an example of a population that has been defined.

Obviously, the population has to be defined correctly. Otherwise, you will be studying the wrong group of people. Not defining the population correctly can result in flawed research, or sampling error. A sampling error is any type of marketing research mistake that results because a sample was utilized. One criticism of Internet surveys is that the people who take these surveys don’t really represent the overall population. On average, Internet survey takers tend to be more educated and tech savvy. Consequently, if they solely constitute your population, even if you screen them for certain criteria, the data you collect could end up being skewed.

The next step is to put together the sampling frame , which is the list from which the sample is drawn. The sampling frame can be put together using a directory, customer list, or membership roster (Wrenn et. al., 2007). Keep in mind that the sampling frame won’t perfectly match the population. Some people will be included on the list who shouldn’t be. Other people who should be included will be inadvertently omitted. It’s no different than if you were to conduct a survey of, say, 25 percent of your friends, using friends’ names you have in your cell phone. Most of your friends’ names are likely to be programmed into your phone, but not all of them. As a result, a certain degree of sampling error always occurs.

There are two main categories of samples in terms of how they are drawn: probability samples and nonprobability samples. A probability sample is one in which each would-be participant has a known and equal chance of being selected. The chance is known because the total number of people in the sampling frame is known. For example, if every other person from the sampling frame were chosen, each person would have a 50 percent chance of being selected.

A nonprobability sample is any type of sample that’s not drawn in a systematic way. So the chances of each would-be participant being selected can’t be known. A convenience sample is one type of nonprobability sample. It is a sample a researcher draws because it’s readily available and convenient to do so. Surveying people on the street as they pass by is an example of a convenience sample. The question is, are these people representative of the target market?

For example, suppose a grocery store needed to quickly conduct some research on shoppers to get ready for an upcoming promotion. Now suppose that the researcher assigned to the project showed up between the hours of 10 a.m. and 12 p.m. on a weekday and surveyed as many shoppers as possible. The problem is that the shoppers wouldn’t be representative of the store’s entire target market. What about commuters who stop at the store before and after work? Their views wouldn’t be represented. Neither would people who work the night shift or shop at odd hours. As a result, there would be a lot of room for sampling error in this study. For this reason, studies that use nonprobability samples aren’t considered as accurate as studies that use probability samples. Nonprobability samples are more often used in exploratory research.

Lastly, the size of the sample has an effect on the amount of sampling error. Larger samples generally produce more accurate results. The larger your sample is, the more data you will have, which will give you a more complete picture of what you’re studying. However, the more people surveyed or studied, the more costly the research becomes.

Statistics can be used to determine a sample’s optimal size. If you take a marketing research or statistics class, you will learn more about how to determine the optimal size.

Of course, if you hire a marketing research company, much of this work will be taken care of for you. Many marketing research companies, like ResearchNow, maintain panels of prescreened people they draw upon for samples. In addition, the marketing research firm will be responsible for collecting the data or contracting with a company that specializes in data collection. Data collection is discussed next.

Step 5: Collect the Data

As we have explained, primary marketing research data can be gathered in a number of ways. Surveys, taking physical measurements, and observing people are just three of the ways we discussed. If you’re observing customers as part of gathering the data, keep in mind that if shoppers are aware of the fact, it can have an effect on their behavior. For example, if a customer shopping for feminine hygiene products in a supermarket aisle realizes she is being watched, she could become embarrassed and leave the aisle, which would adversely affect your data. To get around problems such as these, some companies set up cameras or two-way mirrors to observe customers. Organizations also hire mystery shoppers to work around the problem. A mystery shopper is someone who is paid to shop at a firm’s establishment or one of its competitors to observe the level of service, cleanliness of the facility, and so forth, and report his or her findings to the firm.

Make Extra Money as a Mystery Shopper

Watch the YouTube video to get an idea of how mystery shopping works.

Survey data can be collected in many different ways and combinations of ways. The following are the basic methods used:

• Face-to-face (can be computer aided)
• Telephone (can be computer aided or completely automated)
• Mail and hand delivery
• E-mail and the Web

A face-to-face survey is, of course, administered by a person. The surveys are conducted in public places such as in shopping malls, on the street, or in people’s homes if they have agreed to it. In years past, it was common for researchers in the United States to knock on people’s doors to gather survey data. However, randomly collected door-to-door interviews are less common today, partly because people are afraid of crime and are reluctant to give information to strangers (McDaniel & Gates, 1998).

Nonetheless, “beating the streets” is still a legitimate way questionnaire data is collected. When the U.S. Census Bureau collects data on the nation’s population, it hand delivers questionnaires to rural households that do not have street-name and house-number addresses. And Census Bureau workers personally survey the homeless to collect information about their numbers. Face-to-face surveys are also commonly used in third world countries to collect information from people who cannot read or lack phones and computers.

A plus of face-to-face surveys is that they allow researchers to ask lengthier, more complex questions because the people being surveyed can see and read the questionnaires. The same is true when a computer is utilized. For example, the researcher might ask the respondent to look at a list of ten retail stores and rank the stores from best to worst. The same question wouldn’t work so well over the telephone because the person couldn’t see the list. The question would have to be rewritten. Another drawback with telephone surveys is that even though federal and state “do not call” laws generally don’t prohibit companies from gathering survey information over the phone, people often screen such calls using answering machines and caller ID.

Probably the biggest drawback of both surveys conducted face-to-face and administered over the phone by a person is that they are labor intensive and therefore costly. Mailing out questionnaires is costly, too, and the response rates can be rather low. Think about why that might be so: if you receive a questionnaire in the mail, it is easy to throw it in the trash; it’s harder to tell a market researcher who approaches you on the street that you don’t want to be interviewed.

By contrast, gathering survey data collected by a computer, either over the telephone or on the Internet, can be very cost-effective and in some cases free. SurveyMonkey and Zoomerang are two Web sites that will allow you to create online questionnaires, e-mail them to up to one hundred people for free, and view the responses in real time as they come in. For larger surveys, you have to pay a subscription price of a few hundred dollars. But that still can be extremely cost-effective. The two Web sites also have a host of other features such as online-survey templates you can use to create your questionnaire, a way to set up automatic reminders sent to people who haven’t yet completed their surveys, and tools you can use to create graphics to put in your final research report. To see how easy it is to put together a survey in SurveyMonkey, click on the following link: http://help.surveymonkey.com/app/tutorials/detail/a_id/423 .

Like a face-to-face survey, an Internet survey can enable you to show buyers different visuals such as ads, pictures, and videos of products and their packaging. Web surveys are also fast, which is a major plus. Whereas face-to-face and mailed surveys often take weeks to collect, you can conduct a Web survey in a matter of days or even hours. And, of course, because the information is electronically gathered it can be automatically tabulated. You can also potentially reach a broader geographic group than you could if you had to personally interview people. The Zoomerang Web site allows you to create surveys in forty different languages.

Another plus for Web and computer surveys (and electronic phone surveys) is that there is less room for human error because the surveys are administered electronically. For instance, there’s no risk that the interviewer will ask a question wrong or use a tone of voice that could mislead the respondents. Respondents are also likely to feel more comfortable inputting the information into a computer if a question is sensitive than they would divulging the information to another person face-to-face or over the phone. Given all of these advantages, it’s not surprising that the Internet is quickly becoming the top way to collect primary data. However, like mail surveys, surveys sent to people over the Internet are easy to ignore.

Lastly, before the data collection process begins, the surveyors and observers need to be trained to look for the same things, ask questions the same way, and so forth. If they are using rankings or rating scales, they need to be “on the same page,” so to speak, as to what constitutes a high ranking or a low ranking. As an analogy, you have probably had some teachers grade your college papers harder than others. The goal of training is to avoid a wide disparity between how different observers and interviewers record the data.

Figure 10.11

Training people so they know what constitutes different ratings when they are collecting data will improve the quality of the information gathered in a marketing research study.

Ricardo Rodriquez – Satisfaction survey – CC BY-NC-ND 2.0.

For example, if an observation form asks the observers to describe whether a shopper’s behavior is hurried, moderately hurried, or unhurried, they should be given an idea of what defines each rating. Does it depend on how much time the person spends in the store or in the individual aisles? How fast they walk? In other words, the criteria and ratings need to be spelled out.

Collecting International Marketing Research Data

Gathering marketing research data in foreign countries poses special challenges. However, that doesn’t stop firms from doing so. Marketing research companies are located all across the globe, in fact. Eight of the ten largest marketing research companies in the world are headquartered in the United States. However, five of these eight firms earn more of their revenues abroad than they do in the United States. There’s a reason for this: many U.S. markets were saturated, or tapped out, long ago in terms of the amount that they can grow. Coke is an example. As you learned earlier in the book, most of the Coca-Cola Company’s revenues are earned in markets abroad. To be sure, the United States is still a huge market when it comes to the revenues marketing research firms generate by conducting research in the country: in terms of their spending, American consumers fuel the world’s economic engine. Still, emerging countries with growing middle classes, such as China, India, and Brazil, are hot new markets companies want to tap.

What kind of challenges do firms face when trying to conduct marketing research abroad? As we explained, face-to-face surveys are commonly used in third world countries to collect information from people who cannot read or lack phones and computers. However, face-to-face surveys are also common in Europe, despite the fact that phones and computers are readily available. In-home surveys are also common in parts of Europe. By contrast, in some countries, including many Asian countries, it’s considered taboo or rude to try to gather information from strangers either face-to-face or over the phone. In many Muslim countries, women are forbidden to talk to strangers.

And how do you figure out whom to research in foreign countries? That in itself is a problem. In the United States, researchers often ask if they can talk to the heads of households to conduct marketing research. But in countries in which domestic servants or employees are common, the heads of households aren’t necessarily the principal shoppers; their domestic employees are (Malhotra).

Translating surveys is also an issue. Have you ever watched the TV comedians Jay Leno and David Letterman make fun of the English translations found on ethnic menus and products? Research tools such as surveys can suffer from the same problem. Hiring someone who is bilingual to translate a survey into another language can be a disaster if the person isn’t a native speaker of the language to which the survey is being translated.

One way companies try to deal with translation problems is by using back translation. When back translation is used, a native speaker translates the survey into the foreign language and then translates it back again to the original language to determine if there were gaps in meaning—that is, if anything was lost in translation. And it’s not just the language that’s an issue. If the research involves any visual images, they, too, could be a point of confusion. Certain colors, shapes, and symbols can have negative connotations in other countries. For example, the color white represents purity in many Western cultures, but in China, it is the color of death and mourning (Zouhali-Worrall, 2008). Also, look back at the cartoon-completion exercise in Figure 10.8 “Example of a Cartoon-Completion Projective Technique” . What would women in Muslim countries who aren’t allowed to converse with male sellers think of it? Chances are, the cartoon wouldn’t provide you with the information you’re seeking if Muslim women in some countries were asked to complete it.

One way marketing research companies are dealing with the complexities of global research is by merging with or acquiring marketing research companies abroad. The Nielsen Company is the largest marketing research company in the world. The firm operates in more than a hundred countries and employs more than forty thousand people. Many of its expansions have been the result of acquisitions and mergers.

Step 6: Analyze the Data

Step 6 involves analyzing the data to ensure it’s as accurate as possible. If the research is collected by hand using a pen and pencil, it’s entered into a computer. Or respondents might have already entered the information directly into a computer. For example, when Toyota goes to an event such as a car show, the automaker’s marketing personnel ask would-be buyers to complete questionnaires directly on computers. Companies are also beginning to experiment with software that can be used to collect data using mobile phones.

Once all the data is collected, the researchers begin the data cleaning , which is the process of removing data that have accidentally been duplicated (entered twice into the computer) or correcting data that have obviously been recorded wrong. A program such as Microsoft Excel or a statistical program such as Predictive Analytics Software (PASW, which was formerly known as SPSS) is then used to tabulate, or calculate, the basic results of the research, such as the total number of participants and how collectively they answered various questions. The programs can also be used to calculate averages, such as the average age of respondents, their average satisfaction, and so forth. The same can done for percentages, and other values you learned about, or will learn about, in a statistics course, such as the standard deviation, mean, and median for each question.

The information generated by the programs can be used to draw conclusions, such as what all customers might like or not like about an offering based on what the sample group liked or did not like. The information can also be used to spot differences among groups of people. For example, the research might show that people in one area of the country like the product better than people in another area. Trends to predict what might happen in the future can also be spotted.

If there are any open-ended questions respondents have elaborated upon—for example, “Explain why you like the current brand you use better than any other brand”—the answers to each are pasted together, one on top of another, so researchers can compare and summarize the information. As we have explained, qualitative information such as this can give you a fuller picture of the results of the research.

Part of analyzing the data is to see if it seems sound. Does the way in which the research was conducted seem sound? Was the sample size large enough? Are the conclusions that become apparent from it reasonable?

The two most commonly used criteria used to test the soundness of a study are (1) validity and (2) reliability. A study is valid if it actually tested what it was designed to test. For example, did the experiment you ran in Second Life test what it was designed to test? Did it reflect what could really happen in the real world? If not, the research isn’t valid. If you were to repeat the study, and get the same results (or nearly the same results), the research is said to be reliable . If you get a drastically different result if you repeat the study, it’s not reliable. The data collected, or at least some it, can also be compared to, or reconciled with, similar data from other sources either gathered by your firm or by another organization to see if the information seems on target.

Stage 7: Write the Research Report and Present Its Findings

If you end up becoming a marketing professional and conducting a research study after you graduate, hopefully you will do a great job putting the study together. You will have defined the problem correctly, chosen the right sample, collected the data accurately, analyzed it, and your findings will be sound. At that point, you will be required to write the research report and perhaps present it to an audience of decision makers. You will do so via a written report and, in some cases, a slide or PowerPoint presentation based on your written report.

The six basic elements of a research report are as follows.

• Title Page . The title page explains what the report is about, when it was conducted and by whom, and who requested it.
• Table of Contents . The table of contents outlines the major parts of the report, as well as any graphs and charts, and the page numbers on which they can be found.
• Executive Summary . The executive summary summarizes all the details in the report in a very quick way. Many people who receive the report—both executives and nonexecutives—won’t have time to read the entire report. Instead, they will rely on the executive summary to quickly get an idea of the study’s results and what to do about those results.

Methodology and Limitations . The methodology section of the report explains the technical details of how the research was designed and conducted. The section explains, for example, how the data was collected and by whom, the size of the sample, how it was chosen, and whom or what it consisted of (e.g., the number of women versus men or children versus adults). It also includes information about the statistical techniques used to analyze the data.

Every study has errors—sampling errors, interviewer errors, and so forth. The methodology section should explain these details, so decision makers can consider their overall impact. The margin of error is the overall tendency of the study to be off kilter—that is, how far it could have gone wrong in either direction. Remember how newscasters present the presidential polls before an election? They always say, “This candidate is ahead 48 to 44 percent, plus or minus 2 percent.” That “plus or minus” is the margin of error. The larger the margin of error is, the less likely the results of the study are accurate. The margin of error needs to be included in the methodology section.

• Findings . The findings section is a longer, fleshed-out version of the executive summary that goes into more detail about the statistics uncovered by the research that bolster the study’s findings. If you have related research or secondary data on hand that back up the findings, it can be included to help show the study did what it was designed to do.
• Recommendations . The recommendations section should outline the course of action you think should be taken based on the findings of the research and the purpose of the project. For example, if you conducted a global market research study to identify new locations for stores, make a recommendation for the locations (Mersdorf, 2009).

As we have said, these are the basic sections of a marketing research report. However, additional sections can be added as needed. For example, you might need to add a section on the competition and each firm’s market share. If you’re trying to decide on different supply chain options, you will need to include a section on that topic.

As you write the research report, keep your audience in mind. Don’t use technical jargon decision makers and other people reading the report won’t understand. If technical terms must be used, explain them. Also, proofread the document to ferret out any grammatical errors and typos, and ask a couple of other people to proofread behind you to catch any mistakes you might have missed. If your research report is riddled with errors, its credibility will be undermined, even if the findings and recommendations you make are extremely accurate.

Many research reports are presented via PowerPoint. If you’re asked to create a slideshow presentation from the report, don’t try to include every detail in the report on the slides. The information will be too long and tedious for people attending the presentation to read through. And if they do go to the trouble of reading all the information, they probably won’t be listening to the speaker who is making the presentation.

Instead of including all the information from the study in the slides, boil each section of the report down to key points and add some “talking points” only the presenter will see. After or during the presentation, you can give the attendees the longer, paper version of the report so they can read the details at a convenient time, if they choose to.

Key Takeaway

Step 1 in the marketing research process is to define the problem. Businesses take a look at what they believe are symptoms and try to drill down to the potential causes so as to precisely define the problem. The next task for the researcher is to put into writing the research objective, or goal, the research is supposed to accomplish. Step 2 in the process is to design the research. The research design is the “plan of attack.” It outlines what data you are going to gather, from whom, how, and when, and how you’re going to analyze it once it has been obtained. Step 3 is to design the data-collection forms, which need to be standardized so the information gathered on each is comparable. Surveys are a popular way to gather data because they can be easily administered to large numbers of people fairly quickly. However, to produce the best results, survey questionnaires need to be carefully designed and pretested before they are used. Step 4 is drawing the sample, or a subset of potential buyers who are representative of your entire target market. If the sample is not correctly selected, the research will be flawed. Step 5 is to actually collect the data, whether it’s collected by a person face-to-face, over the phone, or with the help of computers or the Internet. The data-collection process is often different in foreign countries. Step 6 is to analyze the data collected for any obvious errors, tabulate the data, and then draw conclusions from it based on the results. The last step in the process, Step 7, is writing the research report and presenting the findings to decision makers.

Review Questions

• Explain why it’s important to carefully define the problem or opportunity a marketing research study is designed to investigate.
• Describe the different types of problems that can occur when marketing research professionals develop questions for surveys.
• How does a probability sample differ from a nonprobability sample?
• What makes a marketing research study valid? What makes a marketing research study reliable?
• What sections should be included in a marketing research report? What is each section designed to do?

1 “Questionnaire Design,” QuickMBA , http://www.quickmba.com/marketing/research/qdesign (accessed December 14, 2009).

Barnes, B., “Disney Expert Uses Science to Draw Boy Viewers,” New York Times , April 15, 2009, http://www.nytimes.com/2009/04/14/arts/television/14boys.html?pagewanted=1&_r=1 (accessed December 14, 2009).

Burns A. and Ronald Bush, Marketing Research , 6th ed. (Upper Saddle River, NJ: Prentice Hall, 2010), 85.

Malhotra, N., Marketing Research: An Applied Approach , 6th ed. (Upper Saddle River, NJ: Prentice Hall), 764.

McDaniel, C. D. and Roger H. Gates, Marketing Research Essentials , 2nd ed. (Cincinnati: South-Western College Publishing, 1998), 61.

McWilliams, J., “A-B Puts Super-Low-Calorie Beer in Ring with Miller,” St. Louis Post-Dispatch , August 16, 2009, http://www.stltoday.com/business/next-matchup-light-weights-a-b-puts-super-low-calorie/article_47511bfe-18ca-5979-bdb9-0526c97d4edf.html (accessed April 13, 2012).

Mersdorf, S., “How to Organize Your Next Survey Report,” Cvent , August 24, 2009, http://survey.cvent.com/blog/cvent-survey/0/0/how-to-organize-your-next-survey-report (accessed December 14, 2009).

Rappeport A. and David Gelles, “Facebook to Form Alliance with Nielsen,” Financial Times , September 23, 2009, 16.

Spangler, T., “Disney Lab Tracks Feelings,” Multichannel News 30, no. 30 (August 3, 2009): 26.

Wagner, J., “Marketing in Second Life Doesn’t Work…Here Is Why!” GigaOM , April 4, 2007, http://gigaom.com/2007/04/04/3-reasons-why-marketing-in-second-life-doesnt-work (accessed December 14, 2009).

Wrenn, B., Robert E. Stevens, and David L. Loudon, Marketing Research: Text and Cases , 2nd ed. (Binghamton, NY: Haworth Press, 2007), 180.

Zouhali-Worrall, M., “Found in Translation: Avoiding Multilingual Gaffes,” CNNMoney.com , July 14, 2008, http://money.cnn.com/2008/07/07/smallbusiness/language_translation.fsb/index.htm (accessed December 14, 2009).

Principles of Marketing Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

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What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

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From Hypothesis to Results: Mastering the Art of Marketing Experiments

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Suppose you’re trying to convince your friend to watch your favorite movie. You could either tell them about the intriguing plot or show them the exciting trailer.

To find out which approach works best, you try both methods with different friends and see which one gets more people to watch the movie.

Marketing experiments work in much the same way, allowing businesses to test different marketing strategies, gather feedback from their target audience, and make data-driven decisions that lead to improved outcomes and growth.

By testing different approaches and measuring their outcomes, companies can identify what works best for their unique target audience and adapt their marketing strategies accordingly. This leads to more efficient use of marketing resources and results in higher conversion rates, increased customer satisfaction, and, ultimately, business growth.

Marketing experiments are the backbone of building an organization’s culture of learning and curiosity, encouraging employees to think outside the box and challenge the status quo.

In this article, we will delve into the fundamentals of marketing experiments, discussing their key elements and various types. By the end, you’ll be in a position to start running these tests and securing better marketing campaigns with explosive results.

Why Digital Marketing Experiments Matter

One of the most effective ways to drive growth and optimize marketing strategies is through digital marketing experiments. These experiments provide invaluable insights into customer preferences, behaviors, and the overall effectiveness of marketing efforts, making them an essential component of any digital marketing strategy.

Digital marketing experiments matter for several reasons:

• Customer-centric approach: By conducting experiments, businesses can gain a deeper understanding of their target audience’s preferences and behaviors. This enables them to tailor their marketing efforts to better align with customer needs, resulting in more effective and engaging campaigns.
• Data-driven decision-making: Marketing experiments provide quantitative data on the performance of different marketing strategies and tactics. This empowers businesses to make informed decisions based on actual results rather than relying on intuition or guesswork. Ultimately, this data-driven approach leads to more efficient allocation of resources and improved marketing outcomes.
• Agility and adaptability: Businesses must be agile and adaptable to keep up with emerging trends and technologies. Digital marketing experiments allow businesses to test new ideas, platforms, and strategies in a controlled environment, helping them stay ahead of the curve and quickly respond to changing market conditions.
• Continuous improvement: Digital marketing experiments facilitate an iterative process of testing, learning, and refining marketing strategies. This ongoing cycle of improvement enables businesses to optimize their marketing efforts, drive better results, and maintain a competitive edge in the digital marketplace.
• ROI and profitability: By identifying which marketing tactics are most effective, businesses can allocate their marketing budget more efficiently and maximize their return on investment. This increased profitability can be reinvested into the business, fueling further growth and success.

Developing a culture of experimentation allows businesses to continuously improve their marketing strategies, maximize their ROI, and avoid being left behind by the competition.

The Fundamentals of Digital Marketing Experiments

Marketing experiments are structured tests that compare different marketing strategies, tactics, or assets to determine which one performs better in achieving specific objectives.

These experiments use a scientific approach, which involves formulating hypotheses, controlling variables, gathering data, and analyzing the results to make informed decisions.

Marketing experiments provide valuable insights into customer preferences and behaviors, enabling businesses to optimize their marketing efforts and maximize returns on investment (ROI).

There are several types of marketing experiments that businesses can use, depending on their objectives and available resources.

The most common types include:

A/B testing

A/B testing, also known as split testing, is a simple yet powerful technique that compares two variations of a single variable to determine which one performs better.

In an A/B test, the target audience is randomly divided into two groups: one group is exposed to version A (the control). In contrast, the other group is exposed to version B (the treatment). The performance of both versions is then measured and compared to identify the one that yields better results.

A/B testing can be applied to various marketing elements, such as headlines, calls-to-action, email subject lines, landing page designs, and ad copy. The primary advantage of A/B testing is its simplicity, making it easy for businesses to implement and analyze.

Multivariate testing

Multivariate testing is a more advanced technique that allows businesses to test multiple variables simultaneously.

In a multivariate test, several elements of a marketing asset are modified and combined to create different versions. These versions are then shown to different segments of the target audience, and their performance is measured and compared to determine the most effective combination of variables.

Multivariate testing is beneficial when optimizing complex marketing assets, such as websites or email templates, with multiple elements that may interact with one another. However, this method requires a larger sample size and more advanced analytical tools compared to A/B testing.

Pre-post analysis

Pre-post analysis involves comparing the performance of a marketing strategy before and after implementing a change.

This type of experiment is often used when it is not feasible to conduct an A/B or multivariate test, such as when the change affects the entire customer base or when there are external factors that cannot be controlled.

While pre-post analysis can provide useful insights, it is less reliable than A/B or multivariate testing because it does not account for potential confounding factors. To obtain accurate results from a pre-post analysis, businesses must carefully control for external influences and ensure that the observed changes are indeed due to the implemented modifications.

How To Start Growth Marketing Experiments

To conduct effective marketing experiments, businesses must pay attention to the following key elements:

Clear objectives

Having clear objectives is crucial for a successful marketing experiment. Before starting an experiment, businesses must identify the specific goals they want to achieve, such as increasing conversions, boosting engagement, or improving click-through rates. Clear objectives help guide the experimental design and ensure the results are relevant and actionable.

Hypothesis-driven approach

A marketing experiment should be based on a well-formulated hypothesis that predicts the expected outcome. A reasonable hypothesis is specific, testable, and grounded in existing knowledge or data. It serves as the foundation for experimental design and helps businesses focus on the most relevant variables and outcomes.

Proper experimental design

A marketing experiment requires a well-designed test that controls for potential confounding factors and ensures the reliability and validity of the results. This includes the random assignment of participants, controlling for external influences, and selecting appropriate variables to test. Proper experimental design increases the likelihood that observed differences are due to the tested variables and not other factors.

Adequate sample size

A successful marketing experiment requires an adequate sample size to ensure the results are statistically significant and generalizable to the broader target audience. The required sample size depends on the type of experiment, the expected effect size, and the desired level of confidence. In general, larger sample sizes provide more reliable and accurate results but may also require more resources to conduct the experiment.

Data-driven analysis

Marketing experiments rely on a data-driven analysis of the results. This involves using statistical techniques to determine whether the observed differences between the tested variations are significant and meaningful. Data-driven analysis helps businesses make informed decisions based on empirical evidence rather than intuition or gut feelings.

By understanding the fundamentals of marketing experiments and following best practices, businesses can gain valuable insights into customer preferences and behaviors, ultimately leading to improved outcomes and growth.

Setting up Your First Marketing Experiment

Embarking on your first marketing experiment can be both exciting and challenging. Following a systematic approach, you can set yourself up for success and gain valuable insights to improve your marketing efforts.

Here’s a step-by-step guide to help you set up your first marketing experiment.

Identifying your marketing objectives

Before diving into your experiment, it’s essential to establish clear marketing objectives. These objectives will guide your entire experiment, from hypothesis formulation to data analysis.

Consider what you want to achieve with your marketing efforts, such as increasing website conversions, improving open email rates, or boosting social media engagement.

Make sure your objectives are specific, measurable, achievable, relevant, and time-bound (SMART) to ensure that they are actionable and provide meaningful insights.

Formulating a hypothesis

With your marketing objectives in mind, the next step is formulating a hypothesis for your experiment. A hypothesis is a testable prediction that outlines the expected outcome of your experiment. It should be based on existing knowledge, data, or observations and provide a clear direction for your experimental design.

For example, suppose your objective is to increase email open rates. In that case, your hypothesis might be, “Adding the recipient’s first name to the email subject line will increase the open rate by 10%.” This hypothesis is specific, testable, and clearly linked to your marketing objective.

Designing the experiment

Once you have a hypothesis in place, you can move on to designing your experiment. This involves several key decisions:

Choosing the right testing method:

Select the most appropriate testing method for your experiment based on your objectives, hypothesis, and available resources.

As discussed earlier, common testing methods include A/B, multivariate, and pre-post analyses. Choose the method that best aligns with your goals and allows you to effectively test your hypothesis.

Selecting the variables to test:

Identify the specific variables you will test in your experiment. These should be directly related to your hypothesis and marketing objectives. In the email open rate example, the variable to test would be the subject line, specifically the presence or absence of the recipient’s first name.

When selecting variables, consider their potential impact on your marketing objectives and prioritize those with the greatest potential for improvement. Also, ensure that the variables are easily measurable and can be manipulated in your experiment.

Identifying the target audience:

Determine the target audience for your experiment, considering factors such as demographics, interests, and behaviors. Your target audience should be representative of the larger population you aim to reach with your marketing efforts.

When segmenting your audience for the experiment, ensure that the groups are as similar as possible to minimize potential confounding factors.

In A/B or multivariate testing, this can be achieved through random assignment, which helps control for external influences and ensures a fair comparison between the tested variations.

Executing the experiment

With your experiment designed, it’s time to put it into action.

This involves several key considerations:

Timing and duration:

Choose the right timing and duration for your experiment based on factors such as the marketing channel, target audience, and the nature of the tested variables.

The duration of the experiment should be long enough to gather a sufficient amount of data for meaningful analysis but not so long that it negatively affects your marketing efforts or causes fatigue among your target audience.

In general, aim for a duration that allows you to reach a predetermined sample size or achieve statistical significance. This may vary depending on the specific experiment and the desired level of confidence.

Monitoring the experiment:

During the experiment, monitor its progress and performance regularly to ensure that everything is running smoothly and according to plan. This includes checking for technical issues, tracking key metrics, and watching for any unexpected patterns or trends.

If any issues arise during the experiment, address them promptly to prevent potential biases or inaccuracies in the results. Additionally, avoid making changes to the experimental design or variables during the experiment, as this can compromise the integrity of the results.

Analyzing the results

Once your experiment has concluded, it’s time to analyze the data and draw conclusions.

This involves two key aspects:

Statistical significance:

Statistical significance is a measure of the likelihood that the observed differences between the tested variations are due to the variables being tested rather than random chance. To determine statistical significance, you will need to perform a statistical test, such as a t-test or chi-squared test, depending on the nature of your data.

Generally, a result is considered statistically significant if the probability of the observed difference occurring by chance (the p-value) is less than a predetermined threshold, often set at 0.05 or 5%. This means there is a 95% confidence level that the observed difference is due to the tested variables and not random chance.

Practical significance:

While statistical significance is crucial, it’s also essential to consider the practical significance of your results. This refers to the real-world impact of the observed differences on your marketing objectives and business goals.

To assess practical significance, consider the effect size of the observed difference (e.g., the percentage increase in email open rates) and the potential return on investment (ROI) of implementing the winning variation. This will help you determine whether the experiment results are worth acting upon and inform your marketing decisions moving forward.

A systematic approach to designing growth marketing experiments helps you to design, execute, and analyze your experiment effectively, ultimately leading to better marketing outcomes and business growth.

Examples of Successful Marketing Experiments

In this section, we will explore three fictional case studies of successful marketing experiments that led to improved marketing outcomes. These examples will demonstrate the practical application of marketing experiments across different channels and provide valuable lessons that can be applied to your own marketing efforts.

Example 1: Redesigning a website for increased conversions

AcmeWidgets, an online store selling innovative widgets, noticed that its website conversion rate had plateaued.

They conducted a marketing experiment to test whether a redesigned landing page could improve conversions. They hypothesized that a more visually appealing and user-friendly design would increase conversion rates by 15%.

AcmeWidgets used A/B testing to compare their existing landing page (the control) with a new, redesigned version (the treatment). They randomly assigned website visitors to one of the two landing pages. They tracked conversions over a period of four weeks.

At the end of the experiment, AcmeWidgets found that the redesigned landing page had a conversion rate 18% higher than the control. The results were statistically significant, and the company decided to implement the new design across its entire website.

As a result, AcmeWidgets experienced a substantial increase in sales and revenue.

Example 2: Optimizing email marketing campaigns

EcoTravel, a sustainable travel agency, wanted to improve the open rates of their monthly newsletter. They hypothesized that adding a sense of urgency to the subject line would increase open rates by 10%.

To test this hypothesis, EcoTravel used A/B testing to compare two different subject lines for their newsletter:

• “Discover the world’s most beautiful eco-friendly destinations” (control)
• “Last chance to book: Explore the world’s most beautiful eco-friendly destinations” (treatment)

EcoTravel sent the newsletter to a random sample of their subscribers. Half received the control subject line, and the other half received the treatment. They then tracked the open rates for both groups over one week.

The results of the experiment showed that the treatment subject line, which included a sense of urgency, led to a 12% increase in open rates compared to the control.

Based on these findings, EcoTravel incorporated a sense of urgency in their future email subject lines to boost newsletter engagement.

Example 3: Improving social media ad performance

FitFuel, a meal delivery service for fitness enthusiasts, was looking to improve its Facebook ad campaign’s click-through rate (CTR). They hypothesized that using an image of a satisfied customer enjoying a FitFuel meal would increase CTR by 8% compared to their current ad featuring a meal image alone.

FitFuel conducted an A/B test on their Facebook ad campaign, comparing the performance of the control ad (meal image only) with the treatment ad (customer enjoying a meal). They targeted a similar audience with both ad variations and measured the CTR over two weeks. The experiment revealed that the treatment ad, featuring the customer enjoying a meal, led to a 10% increase in CTR compared to the control ad. FitFuel decided to update its

Facebook ad campaign with the new image, resulting in a more cost-effective campaign and higher return on investment.

Lessons learned from these examples

These fictional examples of successful marketing experiments highlight several key takeaways:

• Clearly defined objectives and hypotheses: In each example, the companies had specific marketing objectives and well-formulated hypotheses, which helped guide their experiments and ensure relevant and actionable results.
• Proper experimental design: Each company used the appropriate testing method for their experiment and carefully controlled variables, ensuring accurate and reliable results.
• Data-driven decision-making: The companies analyzed the data from their experiments to make informed decisions about implementing changes to their marketing strategies, ultimately leading to improved outcomes.
• Continuous improvement: These examples demonstrate that marketing experiments can improve marketing efforts continuously. By regularly conducting experiments and applying the lessons learned, businesses can optimize their marketing strategies and stay ahead of the competition.
• Relevance across channels: Marketing experiments can be applied across various marketing channels, such as website design, email campaigns, and social media advertising. Regardless of the channel, the principles of marketing experimentation remain the same, making them a valuable tool for marketers in diverse industries.

By learning from these fictional examples and applying the principles of marketing experimentation to your own marketing efforts, you can unlock valuable insights, optimize your marketing strategies, and achieve better results for your business.

Common Pitfalls of Marketing Experiments and How to Avoid Them

Conducting marketing experiments can be a powerful way to optimize your marketing strategies and drive better results.

However, it’s important to be aware of common pitfalls that can undermine the effectiveness of your experiments. In this section, we will discuss some of these pitfalls and provide tips on how to avoid them.

Insufficient sample size

An insufficient sample size can lead to unreliable results and limit the generalizability of your findings. When your sample size is too small, you run the risk of not detecting meaningful differences between the tested variations or incorrectly attributing the observed differences to random chance.

To avoid this pitfall, calculate the required sample size for your experiment based on factors such as the expected effect size, the desired level of confidence, and the type of statistical test you will use.

In general, larger sample sizes provide more reliable and accurate results but may require more resources to conduct the experiment. Consider adjusting your experimental design or testing methods to accommodate a larger sample size if necessary.

Lack of clear objectives

Your marketing experiment may not provide meaningful or actionable insights without clear objectives. Unclear objectives can lead to poorly designed experiments, irrelevant variables, or difficulty interpreting the results.

To prevent this issue, establish specific, measurable, achievable, relevant, and time-bound (SMART) objectives before starting your experiment. These objectives should guide your entire experiment, from hypothesis formulation to data analysis, and ensure that your findings are relevant and useful for your marketing efforts.

Confirmation bias

Confirmation bias occurs when you interpret the results of your experiment in a way that supports your pre-existing beliefs or expectations. This can lead to inaccurate conclusions and suboptimal marketing decisions.

To minimize confirmation bias, approach your experiments with an open mind and be willing to accept results that challenge your assumptions.

Additionally, involve multiple team members in the data analysis process to ensure diverse perspectives and reduce the risk of individual biases influencing the interpretation of the results.

Overlooking external factors

External factors, such as changes in market conditions, seasonal fluctuations, or competitor actions, can influence the results of your marketing experiment and potentially confound your findings. Ignoring these factors may lead to inaccurate conclusions about the effectiveness of your marketing strategies.

To account for external factors, carefully control for potential confounding variables during the experimental design process. This might involve using random assignment, testing during stable periods, or controlling for known external influences.

Consider running follow-up experiments or analyzing historical data to confirm your findings and rule out the impact of external factors.

Tips for avoiding these pitfalls

By being aware of these common pitfalls and following best practices, you can ensure the success of your marketing experiments and obtain valuable insights for your marketing efforts. Here are some tips to help you avoid these pitfalls:

• Plan your experiment carefully: Invest time in the planning stage to establish clear objectives, calculate an adequate sample size, and design a robust experiment that controls for potential confounding factors.
• Use a hypothesis-driven approach: Formulate a specific, testable hypothesis based on existing knowledge or data to guide your experiment and focus on the most relevant variables and outcomes.
• Monitor your experiment closely: Regularly check the progress of your experiment, address any issues that arise, and ensure that your experiment is running smoothly and according to plan.
• Analyze your data objectively: Use statistical techniques to determine the significance of your results and consider the practical implications of your findings before making marketing decisions.
• Learn from your experiments: Apply the lessons learned from your experiments to continuously improve your marketing strategies and stay ahead of the competition.

By avoiding these common pitfalls and following best practices, you can increase the effectiveness of your marketing experiments, gain valuable insights into customer preferences and behaviors, and ultimately drive better results for your business.

Building a Culture of Experimentation

To truly reap the benefits of marketing experiments, it’s essential to build a culture of experimentation within your organization. This means fostering an environment where curiosity, learning, data-driven decision-making, and collaboration are valued and encouraged.

Encouraging curiosity and learning within your organization

Cultivating curiosity and learning starts with leadership. Encourage your team to ask questions, explore new ideas, and embrace a growth mindset.

Promote ongoing learning by providing resources, such as training programs, workshops, or access to industry events, that help your team stay up-to-date with the latest marketing trends and techniques.

Create a safe environment where employees feel comfortable sharing their ideas and taking calculated risks. Emphasize the importance of learning from both successes and failures and treat every experiment as an opportunity to grow and improve.

Adopting a data-driven mindset

A data-driven mindset is crucial for successful marketing experimentation. Encourage your team to make decisions based on data rather than relying on intuition or guesswork. This means analyzing the results of your experiments objectively, using statistical techniques to determine the significance of your findings, and considering the practical implications of your results before making marketing decisions.

To foster a data-driven culture, invest in the necessary tools and technologies to collect, analyze, and visualize data effectively. Train your team on how to use these tools and interpret the data to make informed marketing decisions.

Regularly review your data-driven efforts and adjust your strategies as needed to continuously improve and optimize your marketing efforts.

Integrating experimentation into your marketing strategy

Establish a systematic approach to conducting marketing experiments to fully integrate experimentation into your marketing strategy. This might involve setting up a dedicated team or working group responsible for planning, executing, and analyzing experiments or incorporating experimentation as a standard part of your marketing processes.

Create a roadmap for your marketing experiments that outlines each project’s objectives, hypotheses, and experimental designs. Monitor the progress of your experiments and adjust your roadmap as needed based on the results and lessons learned.

Ensure that your marketing team has the necessary resources, such as time, budget, and tools, to conduct experiments effectively. Set clear expectations for the role of experimentation in your marketing efforts and emphasize its importance in driving better results and continuous improvement.

Collaborating across teams for a holistic approach

Marketing experiments often involve multiple teams within an organization, such as design, product, sales, and customer support. Encourage cross-functional collaboration to ensure a holistic approach to experimentation and leverage each team’s unique insights and expertise.

Establish clear communication channels and processes for sharing information and results from your experiments. This might involve regular meetings, shared documentation, or internal presentations to keep all stakeholders informed and engaged.

Collaboration also extends beyond your organization. Connect with other marketing professionals, industry experts, and thought leaders to learn from their experiences, share your own insights, and stay informed about the latest trends and best practices in marketing experimentation.

By building a culture of experimentation within your organization, you can unlock valuable insights, optimize your marketing strategies, and drive better results for your business.

Encourage curiosity and learning, adopt a data-driven mindset, integrate experimentation into your marketing strategy, and collaborate across teams to create a strong foundation for marketing success.

If you’re new to marketing experiments, don’t be intimidated—start small and gradually expand your efforts as your confidence grows. By embracing a curious and data-driven mindset, even small-scale experiments can lead to meaningful insights and improvements.

As you gain experience, you can tackle more complex experiments and further refine your marketing strategies.

Remember, continuous learning and improvement is the key to success in marketing experimentation. By regularly conducting experiments, analyzing the results, and applying the lessons learned, you can stay ahead of the competition and drive better results for your business.

So, take the plunge and start experimenting today—your marketing efforts will be all the better.

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Marketing Analytics Lab

Hypothesis Testing in Marketing Research

Introduction to Hypothesis Testing in Marketing Research

Hypothesis testing is a critical component of marketing research that allows marketers to draw conclusions about the effectiveness of their strategies. In essence, hypothesis testing involves making an educated guess about a population parameter and then using data to determine if the hypothesis is supported or rejected. In the context of marketing, hypotheses can be formulated about consumer behavior, product preferences, advertising effectiveness, and many other aspects of the marketing mix. By conducting hypothesis tests, marketers can make informed decisions based on empirical evidence rather than intuition or guesswork.

A hypothesis test in marketing research typically follows a structured process that involves defining a null hypothesis (H0) and an alternative hypothesis (HA), collecting and analyzing data, determining the appropriate statistical test to use, setting a significance level, and interpreting the results to either accept or reject the null hypothesis. The null hypothesis represents the status quo or the assumption that there is no significant difference or relationship between variables, while the alternative hypothesis suggests that there is a significant effect or relationship. By rigorously testing hypotheses, marketers can evaluate the impact of their marketing strategies and make data-driven decisions to optimize their campaigns and initiatives.

The results of hypothesis testing in marketing research provide valuable insights that can inform strategic decision-making and help marketers achieve their business objectives. Whether testing the effectiveness of a new product launch, evaluating the impact of a promotional campaign, or analyzing consumer preferences, hypothesis testing enables marketers to quantify the impact of their actions and make evidence-based recommendations. By employing statistical techniques and hypothesis testing in marketing research, organizations can gain a deeper understanding of consumer behavior, identify market trends, and refine their marketing strategies to drive business growth and success.

Key Steps and Considerations for Hypothesis Testing in Marketing Analysis

When conducting hypothesis testing in marketing research, there are several key steps and considerations that marketers should keep in mind to ensure the validity and reliability of their findings. Firstly, it is essential to clearly define the research question and formulate testable hypotheses that are specific, measurable, and relevant to the marketing objectives. By articulating clear hypotheses, marketers can establish a framework for data collection and analysis that aligns with the research objectives.

Once the hypotheses have been formulated, the next step is to determine the appropriate research design and methodology for data collection. Depending on the nature of the research question and the variables involved, marketers may choose to conduct experiments, surveys, observational studies, or other research methods to gather data. It is crucial to ensure that the data collected is representative of the target population and is collected in a systematic and unbiased manner to generate reliable results.

After collecting the data, marketers can perform statistical analysis to test the hypotheses using techniques such as t-tests, ANOVA, regression analysis, or chi-square tests, among others. It is important to select the appropriate statistical test based on the type of data and the research question being investigated. Additionally, setting a significance level (alpha) is crucial for determining the threshold for accepting or rejecting the null hypothesis. By interpreting the results in the context of the significance level, marketers can make informed decisions about the implications of the findings and their impact on marketing strategies.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Marketing91

What is a Research Hypothesis And How to Write it?

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

A research hypothesis can be defined as a clear, specific and predictive statement that states the possible outcome of a scientific study. The result of the research study is based on previous research studies and can be tested by scientific research.

The research hypothesis is written before the beginning of any scientific research or data collection .

Table of Contents

What is Research Hypothesis?

The research hypothesis is the first step and basis of all research endeavours. The research hypothesis shows what you want to prove with your research study. Therefore, the research hypothesis should be written first before you begin the study, no matter what kind of research study you are conducting.

The research hypothesis shows the direction to the researcher conducting the research. It states what the researcher expects to find from the study. It is a tentative answer that guides the entire research study.

Writing a research hypothesis is not an easy task. It requires skills to write a testable research hypothesis. The researcher is required to study the research done by other researchers on the same subject and find out the loopholes in those researches to make it the basis for their research.

Make sure to consider the general research question posed in the study before jumping directly to write a research hypothesis. Pointing out the exact question can be very difficult for researchers as most researchers are usually not aware of what they are trying to find from their research study. Moreover, the added excitement to conduct the study makes it even more difficult for the researchers to pin down the exact research hypothesis.

There are two primary criteria to develop a reasonable research hypothesis. First, the research hypothesis should be researchable and second; it must be interesting. By researchable, we mean that the question in the research hypothesis statement should be able to be answered with the help of science and the answer to the question should be answerable within a reasonable period.

The research hypothesis being interesting means that the research question should be valuable in the context of the ongoing scientific research of the topic.

Let us learn about the research hypothesis in quantitative and qualitative studies:

Research hypothesis in Quantitative studies

The research hypothesis in a quantitative study consists of one independent variable and one dependent variable, and the research hypothesis mentions the expected relationship between both of the variables.

The independent variable is mentioned first in the research hypothesis followed by explanations and results, etc. and then the dependent variable is specified. Make sure that the variables are referred to in the same order as they are mentioned in the research hypothesis; otherwise, there are chances that your readers get confused while reading your research proposal .

When both variables are used in continuous nature, then it is easy to describe negative or positive relationships between both of them. In the case of categorical variables, the hypothesis statement about which category of independent variables is associated with which group of dependent variables.

It is good to represent the research hypothesis in directional format. That means, the statement is made about the expected relationship between the variables based on past research, the study of existing research, on an educational guess , or only by observation.

Additionally, the null hypothesis can also be used between two variables which state that there is no relationship between the variables. The null hypothesis is the basis of all types of statistical research.

Lastly, a simple research hypothesis for quantitative research should provide a direction for the study of the relationship between two variables. Still, it should also use phrases like “tend to” or “in general” to soften the tone of the hypothesis.

Research hypothesis in qualitative research

The role of the research hypothesis in qualitative research is different as compared to its role in quantitative research. The research hypothesis is not developed at the beginning of the research because of the inductive nature of the qualitative studies.

The research hypothesis is introduced during the iterative process of data collection and the Interpretation of the data. The research hypothesis helps the researchers ask more questions and look for answers for disconfirming evidence.

The qualitative study is dependent on the questions and subquestions asked by the researchers at the beginning of the qualitative research. Generally, in qualitative studies one or two central questions are developed and based on these central questions a series of five to ten subquestions is built and these sub-questions are further used to develop central questions for the research purpose.

In qualitative studies, these questions are directly asked the participant of the research study usually through focus groups or in-depth interviews. This is done to develop an understanding between participants of the study and the researchers. This helps in creating a collaborative experience between the two.

Variables in hypothesis

In research studies like correlational research and experimental studies, a hypothesis shows a relationship between two or more variables. There is an independent variable and a dependent variable.

An independent variable is a variable that a researcher can control and change, whereas, a dependent variable is a variable that the researcher measures and observes.

For example, regular exercise lowers the chances of a heart attack. In this example, the regular exercise is an independent variable and probabilities of occurrence of heart attack is a dependent variable that researchers can measure by observation.

How to develop a reasonable research hypothesis?

A research hypothesis plays an essential role in the research study. Therefore, it is necessary to develop an accurate and precise research hypothesis. In this section, you will learn how to develop a reasonable research hypothesis. The following are the steps involved in developing a research hypothesis.

Step 1. Have a question?

The first step involved in writing a research hypothesis is having a question that you want to answer. This question should be specific and within the scope of your research area. Make sure that the question that you ask is researchable within the time duration of your research study. The examples of research hypothesis questions can be

• Do students who attend classes regularly score more in exams?
• Do people prefer to buy products that have a high price as compared to the other similar products available in the market ?

Step 2. Do some preliminary research:

Preliminary research is conducted before a researcher decides his research hypothesis. In the preliminary research, all the knowledge available about the question is collected by studying the theories and previous studies.

Having this knowledge helps the researchers to form educational assumptions about the outcomes of the research. At this stage, the researcher might prepare a conceptual framework to determine which variable should be studied and what you think is the relationship between the different variables.

The preliminary study also helps the researcher to change the topic if he feels the problem doesn’t have much scope for research.

Step 3. Formulation of hypothesis:

At this stage, the final research hypothesis is formulated. At this stage, the researcher has some idea of what he should expect from the research study. Write the answer to the question of research hypothesis in concise and clear sentences.

The clearer the research hypothesis, the easier will be for researchers to conduct the research.

Step 4. Refine the final hypothesis:

It is essential to make sure that your research hypothesis is testable and specific. You can define a hypothesis in different ways, but you should make sure that all the words that you use in your research hypothesis have precise definitions.

Besides, your hypothesis should contain a set of variables, the relationship between the variables, specific group being studied, and already predicted the outcome of the research.

Step 5. Use three methods to phrase your hypothesis:

They establish a clear relationship between variables, write the hypothesis in if.. then form. The first part of the sentence should be an independent variable, and the second part of the variable should state the dependent variable.

For example, if a student attends 100% classes in a semester, then he will score more than 90% in the exams.

In academic research, the research hypotheses are formed in terms of correlations or effects. In such hypotheses, the relationship between the variables is directly stated in the research hypothesis.

For example, the high numbers of lectures attended by students have a positive impact on their results.

When you are writing a research hypothesis to compare two groups, the hypothesis should state what the differences you are expecting to find in both the groups are.

For example, the students who have more than 70% attendance will score better in exams than the students who have lower than 50% attendance.

Step 6. Write the Null hypothesis:

A null hypothesis is written when research involves statistical hypothesis testing. A null hypothesis when there is no specific relationship between the variables.

It is a default position that shows that two variables used in the hypothesis are not related to each other. A null hypothesis is usually written as H0, and alternative hypotheses are written as H1 or Ha.

Importance of Research Hypothesis

Research plays an essential role in every field. To experiment, a researcher needs to make sure that the research he wants to conduct is testable. A research hypothesis is developed after conducting a preliminary study.

A preliminary study is the study of previous studies done by researchers and the study of research papers written on the same concept. With the help of the research hypothesis, a researcher makes sure that he is not hidden towards a dead end, and it works as a direction map for the researcher.

Liked this post? Check out the complete series on Market research

Related posts:

• How to Write Research Proposal? Research Proposal Format
• What are the Research Objectives? Types, Examples & How to Write Them
• How to write a Research Question? Types and Tips
• What is a Research Statement and How to Write it
• What is Research Design? Type of Research Designs
• 7 Key Differences between Research Method and Research Methodology
• Qualitative Research: Meaning, and Features of Qualitative Research
• Research Ethics – Importance and Principles of Ethics in Research
• What Are Concept Statements? How To Write A Concept Statement?
• Sales Copy: What it is and How to Write Effective Sales Copy

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Marketing Research Design & Analysis 2019

5 hypothesis testing.

This chapter is primarily based on Field, A., Miles J., & Field, Z. (2012): Discovering Statistics Using R. Sage Publications, chapters 5, 9, 15, 18 .

You can download the corresponding R-Code here

5.1 Introduction

We test hypotheses because we are confined to taking samples – we rarely work with the entire population. In the previous chapter, we introduced the standard error (i.e., the standard deviation of a large number of hypothetical samples) as an estimate of how well a particular sample represents the population. We also saw how we can construct confidence intervals around the sample mean \(\bar x\) by computing \(SE_{\bar x}\) as an estimate of \(\sigma_{\bar x}\) using \(s\) as an estimate of \(\sigma\) and calculating the 95% CI as \(\bar x \pm 1.96 * SE_{\bar x}\) . Although we do not know the true population mean ( \(\mu\) ), we might have an hypothesis about it and this would tell us how the corresponding sampling distribution looks like. Based on the sampling distribution of the hypothesized population mean, we could then determine the probability of a given sample assuming that the hypothesis is true .

Let us again begin by assuming we know the entire population using the example of music listening times among students from the previous example. As a reminder, the following plot shows the distribution of music listening times in the population of WU students.

In this example, the population mean ( \(\mu\) ) is equal to 19.98, and the population standard deviation \(\sigma\) is equal to 14.15.

5.1.1 The null hypothesis

Let us assume that we were planning to take a random sample of 50 students from this population and our hypothesis was that the mean listening time is equal to some specific value \(\mu_0\) , say \(10\) . This would be our null hypothesis . The null hypothesis refers to the statement that is being tested and is usually a statement of the status quo, one of no difference or no effect. In our example, the null hypothesis would state that there is no difference between the true population mean \(\mu\) and the hypothesized value \(\mu_0\) (in our example \(10\) ), which can be expressed as follows:

\[ H_0: \mu = \mu_0 \] When conducting research, we are usually interested in providing evidence against the null hypothesis. If we then observe sufficient evidence against it and our estimate is said to be significant. If the null hypothesis is rejected, this is taken as support for the alternative hypothesis . The alternative hypothesis assumes that some difference exists, which can be expressed as follows:

\[ H_1: \mu \neq \mu_0 \] Accepting the alternative hypothesis in turn will often lead to changes in opinions or actions. Note that while the null hypothesis may be rejected, it can never be accepted based on a single test. If we fail to reject the null hypothesis, it means that we simply haven’t collected enough evidence against the null hypothesis to disprove it. In classical hypothesis testing, there is no way to determine whether the null hypothesis is true. Hypothesis testing provides a means to quantify to what extent the data from our sample is in line with the null hypothesis.

In order to quantify the concept of “sufficient evidence” we look at the theoretical distribution of the sample means given our null hypothesis and the sample standard error. Using the available information we can infer the sampling distribution for our null hypothesis. Recall that the standard deviation of the sampling distribution (i.e., the standard error of the mean) is given by \(\sigma_{\bar x}={\sigma \over \sqrt{n}}\) , and thus can be computed as follows:

Since we know from the central limit theorem that the sampling distribution is normal for large enough samples, we can now visualize the expected sampling distribution if our null hypothesis was in fact true (i.e., if the was no difference between the true population mean and the hypothesized mean of 10).

We also know that 95% of the probability is within 1.96 standard deviations from the mean. Values higher than that are rather unlikely, if our hypothesis about the population mean was indeed true. This is shown by the shaded area, also known as the “rejection region”. To test our hypothesis that the population mean is equal to \(10\) , let us take a random sample from the population.

The mean listening time in the sample (black line) \(\bar x\) is 18.59. We can already see from the graphic above that such a value is rather unlikely under the hypothesis that the population mean is \(10\) . Intuitively, such a result would therefore provide evidence against our null hypothesis. But how could we quantify specifically how unlikely it is to obtain such a value and decide whether or not to reject the null hypothesis? Significance tests can be used to provide answers to these questions.

5.1.2 Statistical inference on a sample

5.1.2.1 test statistic, 5.1.2.1.1 z-scores.

Let’s go back to the sampling distribution above. We know that 95% of all values will fall within 1.96 standard deviations from the mean. So if we could express the distance between our sample mean and the null hypothesis in terms of standard deviations, we could make statements about the probability of getting a sample mean of the observed magnitude (or more extreme values). Essentially, we would like to know how many standard deviations ( \(\sigma_{\bar x}\) ) our sample mean ( \(\bar x\) ) is away from the population mean if the null hypothesis was true ( \(\mu_0\) ). This can be formally expressed as follows:

\[ \bar x- \mu_0 = z \sigma_{\bar x} \]

In this equation, z will tell us how many standard deviations the sample mean \(\bar x\) is away from the null hypothesis \(\mu_0\) . Solving for z gives us:

\[ z = {\bar x- \mu_0 \over \sigma_{\bar x}}={\bar x- \mu_0 \over \sigma / \sqrt{n}} \]

This standardized value (or “z-score”) is also referred to as a test statistic . Let’s compute the test statistic for our example above:

To make a decision on whether the difference can be deemed statistically significant, we now need to compare this calculated test statistic to a meaningful threshold. In order to do so, we need to decide on a significance level \(\alpha\) , which expresses the probability of finding an effect that does not actually exist (i.e., Type I Error). You can find a detailed discussion of this point at the end of this chapter. For now, we will adopt the widely accepted significance level of 5% and set \(\alpha\) to 0.05. The critical value for the normal distribution and \(\alpha\) = 0.05 can be computed using the qnorm() function as follows:

We use 0.975 and not 0.95 since we are running a two-sided test and need to account for the rejection region at the other end of the distribution. Recall that for the normal distribution, 95% of the total probability falls within 1.96 standard deviations of the mean, so that higher (absolute) values provide evidence against the null hypothesis. Generally, we speak of a statistically significant effect if the (absolute) calculated test statistic is larger than the (absolute) critical value. We can easily check if this is the case in our example:

Since the absolute value of the calculated test statistic is larger than the critical value, we would reject \(H_0\) and conclude that the true population mean \(\mu\) is significantly different from the hypothesized value \(\mu_0 = 10\) .

5.1.2.1.2 t-statistic

You may have noticed that the formula for the z-score above assumes that we know the true population standard deviation ( \(\sigma\) ) when computing the standard deviation of the sampling distribution ( \(\sigma_{\bar x}\) ) in the denominator. However, the population standard deviation is usually not known in the real world and therefore represents another unknown population parameter which we have to estimate from the sample. We saw in the previous chapter that we usually use \(s\) as an estimate of \(\sigma\) and \(SE_{\bar x}\) as and estimate of \(\sigma_{\bar x}\) . Intuitively, we should be more conservative regarding the critical value that we used above to assess whether we have a significant effect to reflect this uncertainty about the true population standard deviation. That is, the threshold for a “significant” effect should be higher to safeguard against falsely claiming a significant effect when there is none. If we replace \(\sigma_{\bar x}\) by it’s estimate \(SE_{\bar x}\) in the formula for the z-score, we get a new test statistic (i.e, the t-statistic ) with its own distribution (the t-distribution ):

\[ t = {\bar x- \mu_0 \over SE_{\bar x}}={\bar x- \mu_0 \over s / \sqrt{n}} \]

Here, \(\bar X\) denotes the sample mean and \(s\) the sample standard deviation. The t-distribution has more probability in its “tails”, i.e. farther away from the mean. This reflects the higher uncertainty introduced by replacing the population standard deviation by its sample estimate. Intuitively, this is particularly relevant for small samples, since the uncertainty about the true population parameters decreases with increasing sample size. This is reflected by the fact that the exact shape of the t-distribution depends on the degrees of freedom , which is the sample size minus one (i.e., \(n-1\) ). To see this, the following graph shows the t-distribution with different degrees of freedom for a two-tailed test and \(\alpha = 0.05\) . The grey curve shows the normal distribution.

Notice that as \(n\) gets larger, the t-distribution gets closer and closer to the normal distribution, reflecting the fact that the uncertainty introduced by \(s\) is reduced. To summarize, we now have an estimate for the standard deviation of the distribution of the sample mean (i.e., \(SE_{\bar x}\) ) and an appropriate distribution that takes into account the necessary uncertainty (i.e., the t-distribution). Let us now compute the t-statistic according to the formula above:

Notice that the value of the t-statistic is higher compared to the z-score (4.29). This can be attributed to the fact that by using the \(s\) as and estimate of \(\sigma\) , we underestimate the true population standard deviation. Hence, the critical value would need to be larger to adjust for this. This is what the t-distribution does. Let us compute the critical value from the t-distribution with n - 1 degrees of freedom.

Again, we use 0.975 and not 0.95 since we are running a two-sided test and need to account for the rejection region at the other end of the distribution. Notice that the new critical value based on the t-distributionis larger, to reflect the uncertainty when estimating \(\sigma\) from \(s\) . Now we can see that the calculated test statistic is still larger than the critical value.

The following graphics shows that the calculated test statistic (red line) falls into the rejection region so that in our example, we would reject the null hypothesis that the true population mean is equal to \(10\) .

Decision: Reject \(H_0\) , given that the calculated test statistic is larger than critical value.

Something to keep in mind here is the fact the test statistic is a function of the sample size. This, as \(n\) gets large, the test statistic gets larger as well and we are more likely to find a significant effect. This reflects the decrease in uncertainty about the true population mean as our sample size increases.

5.1.2.2 P-values

In the previous section, we computed the test statistic, which tells us how close our sample is to the null hypothesis. The p-value corresponds to the probability that the test statistic would take a value as extreme or more extreme than the one that we actually observed, assuming that the null hypothesis is true . It is important to note that this is a conditional probability : we compute the probability of observing a sample mean (or a more extreme value) conditional on the assumption that the null hypothesis is true. The pnorm() function can be used to compute this probability. It is the cumulative probability distribution function of the `normal distribution. Cumulative probability means that the function returns the probability that the test statistic will take a value less than or equal to the calculated test statistic given the degrees of freedom. However, we are interested in obtaining the probability of observing a test statistic larger than or equal to the calculated test statistic under the null hypothesis (i.e., the p-value). Thus, we need to subtract the cumulative probability from 1. In addition, since we are running a two-sided test, we need to multiply the probability by 2 to account for the rejection region at the other side of the distribution.

This value corresponds to the probability of observing a mean equal to or larger than the one we obtained from our sample, if the null hypothesis was true. As you can see, this probability is very low. A small p-value signals that it is unlikely to observe the calculated test statistic under the null hypothesis. To decide whether or not to reject the null hypothesis, we would now compare this value to the level of significance ( \(\alpha\) ) that we chose for our test. For this example, we adopt the widely accepted significance level of 5%, so any test results with a p-value < 0.05 would be deemed statistically significant. Note that the p-value is directly related to the value of the test statistic. The relationship is such that the higher (lower) the value of the test statistic, the lower (higher) the p-value.

Decision: Reject \(H_0\) , given that the p-value is smaller than 0.05.

5.1.2.3 Confidence interval

For a given statistic calculated for a sample of observations (e.g., listening times), a 95% confidence interval can be constructed such that in 95% of samples, the true value of the true population mean will fall within its limits. If the parameter value specified in the null hypothesis (here \(10\) ) does not lie within the bounds, we reject \(H_0\) . Building on what we learned about confidence intervals in the previous chapter, the 95% confidence interval based on the t-distribution can be computed as follows:

\[ CI_{lower} = {\bar x} - t_{1-{\alpha \over 2}} * SE_{\bar x} \\ CI_{upper} = {\bar x} + t_{1-{\alpha \over 2}} * SE_{\bar x} \]

It is easy to compute this interval manually:

The interpretation of this interval is as follows: if we would (hypothetically) take 100 samples and calculated the mean and confidence interval for each of them, then the true population mean would be included in 95% of these intervals. The CI is informative when reporting the result of your test, since it provides an estimate of the uncertainty associated with the test result. From the test statistic or the p-value alone, it is not easy to judge in which range the true population parameter is located. The CI provides an estimate of this range.

Decision: Reject \(H_0\) , given that the parameter value from the null hypothesis ( \(10\) ) is not included in the interval.

To summarize, you can see that we arrive at the same conclusion (i.e., reject \(H_0\) ), irrespective if we use the test statistic, the p-value, or the confidence interval. However, keep in mind that rejecting the null hypothesis does not prove the alternative hypothesis (we can merely provide support for it). Rather, think of the p-value as the chance of obtaining the data we’ve collected assuming that the null hypothesis is true. You should report the confidence interval to provide an estimate of the uncertainty associated with your test results.

5.1.3 Choosing the right test

The test statistic, as we have seen, measures how close the sample is to the null hypothesis and often follows a well-known distribution (e.g., normal, t, or chi-square). To select the correct test, various factors need to be taken into consideration. Some examples are:

• On what scale are your variables measured (categorical vs. continuous)?
• Do you want to test for relationships or differences?
• If you test for differences, how many groups would you like to test?
• For parametric tests, are the assumptions fulfilled?

The previous discussion used a one sample t-test as an example, which requires that variable is measured on an interval or ratio scale. If you are confronted with other settings, the following flow chart provides a rough guideline on selecting the correct test:

Flowchart for selecting an appropriate test (source: McElreath, R. (2016): Statistical Rethinking, p. 2)

For a detailed overview over the different type of tests, please also refer to this overview by the UCLA.

5.1.3.1 Parametric vs. non-parametric tests

A basic distinction can be made between parametric and non-parametric tests. Parametric tests require that variables are measured on an interval or ratio scale and that the sampling distribution follows a known distribution. Non-Parametric tests on the other hand do not require the sampling distribution to be normally distributed (a.k.a. “assumption free tests”). These tests may be used when the variable of interest is measured on an ordinal scale or when the parametric assumptions do not hold. They often rely on ranking the data instead of analyzing the actual scores. By ranking the data, information on the magnitude of differences is lost. Thus, parametric tests are more powerful if the sampling distribution is normally distributed. In this chapter, we will first focus on parametric tests and cover non-parametric tests later.

5.1.3.2 One-tailed vs. two-tailed test

For some tests you may choose between a one-tailed test versus a two-tailed test . The choice depends on the hypothesis you specified, i.e., whether you specified a directional or a non-directional hypotheses. In the example above, we used a non-directional hypothesis . That is, we stated that the mean is different from the comparison value \(\mu_0\) , but we did not state the direction of the effect. A directional hypothesis states the direction of the effect. For example, we might test whether the population mean is smaller than a comparison value:

\[ H_0: \mu \ge \mu_0 \\ H_1: \mu < \mu_0 \]

Similarly, we could test whether the population mean is larger than a comparison value:

\[ H_0: \mu \le \mu_0 \\ H_1: \mu > \mu_0 \]

Connected to the decision of how to phrase the hypotheses (directional vs. non-directional) is the choice of a one-tailed test versus a two-tailed test . Let’s first think about the meaning of a one-tailed test. Using a significance level of 0.05, a one-tailed test means that 5% of the total area under the probability distribution of our test statistic is located in one tail. Thus, under a one-tailed test, we test for the possibility of the relationship in one direction only, disregarding the possibility of a relationship in the other direction. In our example, a one-tailed test could test either if the mean listening time is significantly larger or smaller compared to the control condition, but not both. Depending on the direction, the mean listening time is significantly larger (smaller) if the test statistic is located in the top (bottom) 5% of its probability distribution.

The following graph shows the critical values that our test statistic would need to surpass so that the difference between the population mean and the comparison value would be deemed statistically significant.

It can be seen that under a one-sided test, the rejection region is at one end of the distribution or the other. In a two-sided test, the rejection region is split between the two tails. As a consequence, the critical value of the test statistic is smaller using a one-tailed test, meaning that it has more power to detect an effect. Having said that, in most applications, we would like to be able catch effects in both directions, simply because we can often not rule out that an effect might exist that is not in the hypothesized direction. For example, if we would conduct a one-tailed test for a mean larger than some specified value but the mean turns out to be substantially smaller, then testing a one-directional hypothesis ($H_0: _0 $) would not allow us to conclude that there is a significant effect because there is not rejection at this end of the distribution.

5.1.4 Summary

As we have seen, the process of hypothesis testing consists of various steps:

• Formulate null and alternative hypotheses
• Select an appropriate test
• Choose the level of significance ( \(\alpha\) )
• Descriptive statistics and data visualization
• Conduct significance test
• Report results and draw a marketing conclusion

In the following, we will go through the individual steps using examples for different tests.

5.2 One sample t-test

The example we used in the introduction was an example of the one sample t-test and we computed all statistics by hand to explain the underlying intuition. When you conduct hypothesis tests using R, you do not need to calculate these statistics by hand, since there are build-in routines to conduct the steps for you. Let us use the same example again to see how you would conduct hypothesis tests in R.

1. Formulate null and alternative hypotheses

The null hypothesis states that there is no difference between the true population mean \(\mu\) and the hypothesized value (i.e., \(10\) ), while the alternative hypothesis states the opposite:

\[ H_0: \mu = 10 \\ H_1: \mu \neq 10 \]

2. Select an appropriate test

Because we would like to test if the mean of a variable is different from a specified threshold, the one-sample t-test is appropriate. The assumptions of the test are 1) that the variable is measured using an interval or ratio scale, and 2) that the sampling distribution is normal. Both assumptions are met since 1) listening time is a ratio scale, and 2) we deem the sample size (n = 50) large enough to assume a normal sampling distribution according to the central limit theorem.

3. Choose the level of significance

We choose the conventional 5% significance level.

4. Descriptive statistics and data visualization

Provide descriptive statistics using the stat.desc() function:

From this, we can already see that the mean is different from the hypothesized value. The question however remains, whether this difference is significantly different, given the sample size and the variability in the data. Since we only have one continuous variable, we can visualize the distribution in a histogram.

5. Conduct significance test

In the beginning of the chapter, we saw, how you could conduct significance test by hand. However, R has built-in routines that you can use to conduct the analyses. The t.test() function can be used to conduct the test. To test if the listening time among WU students was 10, you can use the following code:

Note that if you would have stated a directional hypothesis (i.e., the mean is either greater or smaller than 10 hours), you could easily amend the code to conduct a one sided test by changing the argument alternative from 'two.sided' to either 'less' or 'greater' .

6. Report results and draw a marketing conclusion

Note that the results are the same as above, when we computed the test by hand. You could summarize the results as follows:

On average, the listening times in our sample were different form 10 hours per month (Mean = 18.99 hours, SE = 1.78). This difference was significant t(49) = 5.058, p < .05 (95% CI = [15.42; 22.56]). Based on this evidence, we can conclude that the mean in our sample is significantly lower compared to the hypothesized population mean of \(10\) hours, providing evidence against the null hypothesis.

Note that in the reporting above, the number 49 in parenthesis refers to the degrees of freedom that are available from the output.

5.3 Comparing two means

In the one-sample test above, we tested the hypothesis that the population mean has some specific value \(\mu_0\) using data from only one sample. In marketing (as in many other disciplines), you will often be confronted with a situation where you wish to compare the means of two groups. For example, you may conduct an experiment and randomly split your sample into two groups, one of which receives a treatment (experimental group) while the other doesn’t (control group). In this case, the units (e.g., participants, products) in each group are different (‘between-subjects design’) and the samples are said to be independent. Hence, we would use a independent-means t-test . If you run an experiment with two experimental conditions and the same units (e.g., participants, products) were observed in both experimental conditions, the sample is said to be dependent in the sense that you have the same units in each group (‘within-subjects design’). In this case, we would need to conduct an dependent-means t-test . Both tests are described in the following sections, beginning with the independent-means t-test.

5.3.1 Independent-means t-test

Using an independent-means t-test, we can compare the means of two possibly different populations. It is, for example, quite common for online companies to test new service features by running an experiment and randomly splitting their website visitors into two groups: one is exposed to the website with the new feature (experimental group) and the other group is not exposed to the new feature (control group). This is a typical A/B-Test scenario.

As an example, imagine that a music streaming service would like to introduce a new playlist feature that let’s their users access playlists created by other users. The goal is to analyse how the new service feature impacts the listening time of users. The service randomly splits a representative subset of their users into two groups and collects data about their listening times over one month. Let’s create a data set to simulate such a scenario.

This data set contains two variables: the variable hours indicates the music listening times (in hours) and the variable group indicates from which group the observation comes, where ‘A’ refers to the control group (with the standard service) and ‘B’ refers to the experimental group (with the new playlist feature). Let’s first look at the descriptive statistics by group using the describeBy function:

From this, we can already see that there is a difference in means between groups A and B. We can also see that the number of observations is different, as is the standard deviation. The question that we would like to answer is whether there is a significant difference in mean listening times between the groups. Remember that different users are contained in each group (‘between-subjects design’) and that the observations in one group are independent of the observations in the other group. Before we will see how you can easily conduct an independent-means t-test, let’s go over some theory first.

5.3.1.1 Theory

As a starting point, let us label the unknown population mean of group A (control group) in our experiment \(\mu_1\) , and that of group B (experimental group) \(\mu_2\) . In this setting, the null hypothesis would state that the mean in group A is equal to the mean in group B:

\[ H_0: \mu_1=\mu_2 \]

This is equivalent to stating that the difference between the two groups ( \(\delta\) ) is zero:

\[ H_0: \mu_1 - \mu_2=0=\delta \]

That is, \(\delta\) is the new unknown population parameter, so that the null and alternative hypothesis become:

\[ H_0: \delta = 0 \\ H_1: \delta \ne 0 \]

Remember that we usually don’t have access to the entire population so that we can not observe \(\delta\) and have to estimate is from a sample statistic, which we define as \(d = \bar x_1-\bar x_2\) , i.e., the difference between the sample means from group a ( \(\bar x_1\) ) and group b ( \(\bar x_2\) ). But can we really estimate \(d\) from \(\delta\) ? Remember from the previous chapter, that we could estimate \(\mu\) from \(\bar x\) , because if we (hypothetically) take a larger number of samples, the distribution of the means of these samples (the sampling distribution) will be normally distributed and its mean will be (in the limit) equal to the population mean. It turns out that we can use the same underlying logic here. The above samples were drawn from two different populations with \(\mu_1\) and \(\mu_2\) . Let us compute the difference in means between these two populations:

This means that the true difference between the mean listening times of groups a and b is -7.42. Let us now repeat the exercise from the previous chapter: let us repeatedly draw a large number of \(20,000\) random samples of 100 users from each of these populations, compute the difference (i.e., \(d\) , our estimate of \(\delta\) ), store the difference for each draw and create a histogram of \(d\) .

This gives us the sampling distribution of the mean differences between the samples. You will notice that this distribution follows a normal distribution and is centered around the true difference between the populations. This means that, on average, the difference between two sample means \(d\) is a good estimate of \(\delta\) . In our example, the difference between \(\bar x_1\) and \(\bar x_2\) is:

Now that we have \(d\) as an estimate of \(\delta\) , how can we find out if the observed difference is significantly different from the null hypothesis (i.e., \(\delta = 0\) )?

Recall from the previous section, that the standard deviation of the sampling distribution \(\sigma_{\bar x}\) (i.e., the standard error) gives us indication about the precision of our estimate. Further recall that the standard error can be calculated as \(\sigma_{\bar x}={\sigma \over \sqrt{n}}\) . So how can we calculate the standard error of the difference between two population means? According to the variance sum law , to find the variance of the sampling distribution of differences, we merely need to add together the variances of the sampling distributions of the two populations that we are comparing. To find the standard error, we only need to take the square root of the variance (because the standard error is the standard deviation of the sampling distribution and the standard deviation is the square root of the variance), so that we get:

\[ \sigma_{\bar x_1-\bar x_2} = \sqrt{{\sigma_1^2 \over n_1}+{\sigma_2^2 \over n_2}} \]

But recall that we don’t actually know the true population standard deviation, so we use \(SE_{\bar x_1-\bar x_2}\) as an estimate of \(\sigma_{\bar x_1-\bar x_2}\) :

\[ SE_{\bar x_1-\bar x_2} = \sqrt{{s_1^2 \over n_1}+{s_2^2 \over n_2}} \]

Hence, for our example, we can calculate the standard error as follows:

Recall from above that we can calculate the t-statistic as:

\[ t= {\bar x - \mu_0 \over {s \over \sqrt{n}}} \]

Exchanging \(\bar x\) for \(d\) , we get

\[ t= {(\bar{x}_1 - \bar{x}_2) - (\mu_1 - \mu_2) \over {\sqrt{{s_1^2 \over n_1}+{s_2^2 \over n_2}}}} \]

Note that according to our hypothesis \(\mu_1-\mu_2=0\) , so that we can calculate the t-statistic as:

Following the example of our one sample t-test above, we would now need to compare this calculated test statistic to a critical value in order to assess if \(d\) is sufficiently far away from the null hypothesis to be statistically significant. To do this, we would need to know the exact t-distribution, which depends on the degrees of freedom. The problem is that deriving the degrees of freedom in this case is not that obvious. If we were willing to assume that \(\sigma_1=\sigma_2\) , the correct t-distribution has \(n_1 -1 + n_2-1\) degrees of freedom (i.e., the sum of the degrees of freedom of the two samples). However, because in real life we don not know if \(\sigma_1=\sigma_2\) , we need to account for this additional uncertainty. We will not go into detail here, but R automatically uses a sophisticated approach to correct the degrees of freedom called the Welch’s correction, as we will see in the subsequent application.

5.3.1.2 Application

The section above explained the theory behind the independent-means t-test and showed how to compute the statistics manually. Obviously you don’t have to compute these statistics by hand in this section shows you how to conduct an independent-means t-test in R using the example from above.

We wish to analyze whether there is a significant difference in music listening times between groups A and B. So our null hypothesis is that the means from the two populations are the same (i.e., there is no difference), while the alternative hypothesis states the opposite:

\[ H_0: \mu_1=\mu_2\\ H_1: \mu_1 \ne \mu_2 \]

Since we have a ratio scaled variable (i.e., listening times) and two independent groups, where the mean of one sample is independent of the group of the second sample (i.e., the groups contain different units), the independent-means t-test is appropriate.

We can compute the descriptive statistics for each group separately, using the describeBy() function:

This already shows us that the mean between groups A and B are different. We can visualize the data using a plot of means, boxplot, and a histogram.

To conduct the independent means t-test, we can use the t.test() function:

The results showed that listening times were higher in the experimental group B (Mean = 28.50, SE = 1.7) compared to the control group (Mean = 18.11, SE = 1.22). This means that the listening times were 10.39 hours higher on average in the experimental group (B), compared to the control group (A). An independent-means t-test showed that this difference is significant t(195.73) = -4.9646, p < .05 (95% CI = [-14.514246,-6.261264]).

5.3.2 Dependent-means t-test

While the independent-means t-test is used when different units (e.g., participants, products) were assigned to the different condition, the dependent-means t-test is used when there are two experimental conditions and the same units (e.g., participants, products) were observed in both experimental conditions.

Imagine, for example, a slightly different experimental setup for the above experiment. Imagine that we do not assign different users to the groups, but that a sample of 100 users gets to use the music streaming service with the new feature for one month and we compare the music listening times of these users during the month of the experiment with the listening time in the previous month. Let us generate data for this example:

Note that the data set has almost the same structure as before only that we know have two variables representing the listening times of each user in the month before the experiment and during the month of the experiment when the new feature was tested.

5.3.2.1 Theory

In this case, we want to test the hypothesis that there is no difference in mean the mean listening times between the two months. This can be expressed as follows:

\[ H_0: \mu_D = 0 \\ \] Note that the hypothesis only refers to one population, since both observations come from the same units (i.e., users). To use consistent notation, we replace \(\mu_D\) with \(\delta\) and get:

\[ H_0: \delta = 0 \\ H_1: \delta \neq 0 \]

where \(\delta\) denotes the difference between the observed listening times from the two consecutive months of the same users . As is the previous example, since we do not observe the entire population, we estimate \(\delta\) based on the sample using \(d\) , which is the difference in mean listening time between the two months for our sample. Note that we assume that everything else (e.g., number of new releases) remained constant over the two month to keep it simple. We can show as above that the sampling distribution follows a normal distribution with a mean that is (in the limit) the same as the population mean. This means, again, that the difference in sample means is a good estimate for the difference in population means. Let’s compute a new variable \(d\) , which is the difference between two month.

Note that we now have a new variable, which is the difference in listening times (in hours) between the two months. The mean of this difference is:

Again, we use \(SE_{\bar x}\) as an estimate of \(\sigma_{\bar x}\) :

\[ SE_{\bar d}={s \over \sqrt{n}} \] Hence, we can compute the standard error as:

The test statistic is therefore:

\[ t = {\bar d- \mu_0 \over SE_{\bar d}} \] on 99 (i.e., n-1) degrees of freedom. Now we can compute the t-statistic as follows:

Note that in the case of the dependent-means t-test, we only base our hypothesis on one population and hence there is only one population variance. This is because in the dependent sample test, the observations come from the same observational units (i.e., users). Hence, there is no unsystematic variation due to potential differences between users that were assigned to the experimental groups. This means that the influence of unobserved factors (unsystematic variation) relative to the variation due to the experimental manipulation (systematic variation) is not as strong in the dependent-means test compared to the independent-means test and we don’t need to correct for differences in the population variances.

5.3.2.2 Application

Again, we don’t have to compute all this by hand since the t.test(...) function can be used to do it for us. Now we have to use the argument paired=TRUE to let R know that we are working with dependent observations.

We would like to the test if there is a difference in music listening times between the two consecutive months, so our null hypothesis is that there is no difference, while the alternative hypothesis states the opposite:

\[ H_0: \mu_D = 0 \\ H_0: \mu_D \ne 0 \]

Since we have a ratio scaled variable (i.e., listening times) and two observations of the same group of users (i.e., the groups contain the same units), the dependent-means t-test is appropriate.

We can compute the descriptive statistics for each month separately, using the describe() function:

This already shows us that the mean between the two months are different. We can visiualize the data using a plot of means, boxplot, and a histogram.

To plot the data, we need to do some restructuring first, since the variables are now stored in two different columns (“hours_a” and “hours_b”). This is also known as the “wide” format. To plot the data we need all observations to be stored in one variable. This is also known as the “long” format. We can use the melt(...) function from the reshape2 package to “melt” the two variable into one column to plot the data.

Now we are ready to plot the data:

To conduct the independent means t-test, we can use the t.test() function with the argument paired = TRUE :

On average, the same users used the service more when it included the new feature (M = 25.96, SE = 1.68) compared to the service without the feature (M = 20.99, SE = 1.34). This difference was significant t(99) = 2.3781, p < .05 (95% CI = [0.82, 9.12]).

5.3.3 Further considerations

5.3.3.1 type i and type ii errors.

When choosing the level of significance ( \(\alpha\) ). It is important to note that the choice of the significance level affects the type 1 and type 2 error:

• Type I error: When we believe there is a genuine effect in our population, when in fact there isn’t. Probability of type I error ( \(\alpha\) ) = level of significance.
• Type II error: When we believe that there is no effect in the population, when in fact there is.

This following table shows the possible outcomes of a test (retain vs. reject \(H_0\) ), depending on whether \(H_0\) is true or false in reality.

5.3.3.2 Significance level, sample size, power, and effect size

When you plan to conduct an experiment, there are some factors that are under direct control of the researcher:

• Significance level ( \(\alpha\) ) : The probability of finding an effect that does not genuinely exist.
• Sample size (n) : The number of observations in each group of the experimental design.

Unlike α and n, which are specified by the researcher, the magnitude of β depends on the actual value of the population parameter. In addition, β is influenced by the effect size (e.g., Cohen’s d), which can be used to determine a standardized measure of the magnitude of an observed effect. The following parameters are affected more indirectly:

• Power (1-β) : The probability of finding an effect that does genuinely exists.
• Effect size (d) : Standardized measure of the effect size under the alternate hypothesis.

Although β is unknown, it is related to α. For example, if we would like to be absolutely sure that we do not falsely identify an effect which does not exist (i.e., make a type I error), this means that the probability of identifying an effect that does exist (i.e., 1-β) decreases and vice versa. Thus, an extremely low value of α (e.g., α = 0.0001) will result in intolerably high β errors. A common approach is to set α=0.05 and 1-β=0.80.

Unlike the t-value of our test, the effect size (d) is unaffected by the sample size and can be categorized as follows (see Cohen, J. 1988):

• 0.2 (small effect)
• 0.5 (medium effect)
• 0.8 (large effect)

In order to test more subtle effects (smaller effect sizes), you need a larger sample size compared to the test of more obvious effects. In this paper , you can find a list of examples for different effect sizes and the number of observations you need to reliably find an effect of that magnitude. Although the exact effect size is unknown before the experiment, you might be able to make a guess about the effect size (e.g., based on previous studies).

If you wish to obtain a standardized measure of the effect, you may compute the effect size (Cohen’s d) using the cohensD() function from the lsr package. Using the examples from the independent-means t-test above, we would use:

According to the thresholds defined above, this effect would be judged to be a small-medium effect.

For the dependent-means t-test, we would use:

According to the thresholds defined above, this effect would also be judged to be a small-medium effect.

When constructing an experimental design, your goal should be to maximize the power of the test while maintaining an acceptable significance level and keeping the sample as small as possible. To achieve this goal, you may use the pwr package, which let’s you compute n , d , alpha , and power . You only need to specify three of the four input variables to get the fourth.

For example, what sample size do we need (per group) to identify an effect with d = 0.6, α = 0.05, and power = 0.8:

Or we could ask, what is the power of our test with 51 observations in each group, d = 0.6, and α = 0.05:

5.3.3.3 P-values, stopping rules and p-hacking

From my experience, students tend to place a lot of weight on p-values when interpreting their research findings. It is therefore important to note some points that hopefully help to put the meaning of a “significant” vs. “insignificant” test result into perspective.

Significant result

• Even if the probability of the effect being a chance result is small (e.g., less than .05) it doesn’t necessarily mean that the effect is important.
• Very small and unimportant effects can turn out to be statistically significant if the sample size is large enough.

Insignificant result

• If the probability of the effect occurring by chance is large (greater than .05), the alternative hypothesis is rejected. However, this does not mean that the null hypothesis is true.
• Although an effect might not be large enough to be anything other than a chance finding, it doesn’t mean that the effect is zero.
• In fact, two random samples will always have slightly different means that would deemed to be statistically significant if the samples were large enough.

Thus, you should not base your research conclusion on p-values alone!

It is also crucial to determine the sample size before you run the experiment or before you start your analysis. Why? Consider the following example:

• You run an experiment
• After each respondent you analyze the data and look at the mean difference between the two groups with a t-test
• You stop when you have a significant effect

This is called p-hacking and should be avoided at all costs. Assuming that both groups come from the same population (i.e., there is no difference in the means): What is the likelihood that the result will be significant at some point? In other words, what is the likelihood that you will draw the wrong conclusion from your data that there is an effect, while there is none? This is shown in the following graph using simulated data - the color red indicates significant test results that arise although there is no effect (i.e., false positives).

Figure 5.1: p-hacking (red indicates false positives)

5.4 Comparing several means

This chapter is primarily based on Field, A., Miles J., & Field, Z. (2012): Discovering Statistics Using R. Sage Publications, chapters 10 & 12 .

5.4.1 Introduction

In the previous section we learned how to compare means using a t-test. The t-test has some limitations since it only lets you compare 2 means and you can only use it with one independent variable. However, often we would like to compare means from 3 or more groups. In addition, there may be instances in which you manipulate more than one independent variable. For these applications, ANOVA (ANalysis Of VAriance) can be used. Hence, to conduct ANOVA you need:

• A metric dependent variable (i.e., measured using an interval or ratio scale)
• One or more non-metric (categorical) independent variables (also called factors)

A treatment is a particular combination of factor levels, or categories. One-way ANOVA is used when there is only one categorical variable (factor). In this case, a treatment is the same as a factor level. N-way ANOVA is used with two or more factors. Note that we are only going to talk about a single independent variable in the context of ANOVA. If you have multiple independent variables please refere to the chapter on Regression .

Let’s use an example to see how ANOVA works. Similar to the previous example it is also imaginable that the music streaming service experiments with a recommendation system for user created playlists. We now have three groups, the control group “A” with the current system, treatment group “B” who have access to playlists created by other users but are not shown recommendations and treatment group “C” who are shown recommendations for user created playlists. As always, we load and inspect the data first:

The null hypothesis, typically, is that all means are equal (non-directional hypothesis). Hence, in our case:

\[H_0: \mu_1 = \mu_2 = \mu_3\]

The alternative hypothesis is simply that the means are not all equal, i.e.,

\[H_1: \textrm{Means are not all equal}\]

If you wanted to put this in mathematical notation, you could also write:

\[H_1: \exists {i,j}: {\mu_i \ne \mu_j} \]

To get a first impression if there are any differences in listening times across the experimental groups, we use the describeBy(...) function from the psych package:

In addition, you should visualize the data using appropriate plots:

Figure 5.2: Plot of means

Note that ANOVA is an omnibus test, which means that we test for an overall difference between groups. Hence, the test will only tell you if the group means are different, but it won’t tell you exactly which groups are different from another.

So why don’t we then just conduct a series of t-tests for all combinations of groups (i.e., A vs. B, A vs. C, B vs. C)? The reason is that if we assume each test to be independent, then there is a 5% probability of falsely rejecting the null hypothesis (Type I error) for each test. In our case:

• A vs. B (α = 0.05)
• A vs. C (α = 0.05)
• B vs. C (α = 0.05)

This means that the overall probability of making a Type I error is 1-(0.95 3 ) = 0.143, since the probability of no Type I error is 0.95 for each of the three tests. Consequently, the Type I error probability would be 14.3%, which is above the conventional standard of 5%. This is also known as the family-wise or experiment-wise error.

5.4.2 Decomposing variance

The basic concept underlying ANOVA is the decomposition of the variance in the data. There are three variance components which we need to consider:

• We calculate how much variability there is between scores: Total sum of squares (SS T )
• We then calculate how much of this variability can be explained by the model we fit to the data (i.e., how much variability is due to the experimental manipulation): Model sum of squares (SS M )
• … and how much cannot be explained (i.e., how much variability is due to individual differences in performance): Residual sum of squares (SS R )

The following figure shows the different variance components using a generalized data matrix:

Decomposing variance

The total variation is determined by the variation between the categories (due to our experimental manipulation) and the within-category variation that is due to extraneous factors (e.g., promotion of artists on a social network):

\[SS_T= SS_M+SS_R\]

To get a better feeling how this relates to our data set, we can look at the data in a slightly different way. Specifically, we can use the dcast(...) function from the reshape2 package to convert the data to wide format:

In this example, X 1 from the generalized data matrix above would refer to the factor level “A”, X 2 to the level “B”, and X 3 to the level “C”. Y 11 refers to the first data point in the first row (i.e., “13”), Y 12 to the second data point in the first row (i.e., “21”), etc.. The grand mean ( \(\overline{Y}\) ) and the category means ( \(\overline{Y}_c\) ) can be easily computed:

To see how each variance component can be derived, let’s look at the data again. The following graph shows the individual observations by experimental group:

Figure 5.3: Sum of Squares

5.4.2.1 Total sum of squares

To compute the total variation in the data, we consider the difference between each observation and the grand mean. The grand mean is the mean over all observations in the data set. The vertical lines in the following plot measure how far each observation is away from the grand mean:

Figure 5.4: Total Sum of Squares

The formal representation of the total sum of squares (SS T ) is:

\[ SS_T= \sum_{i=1}^{N} (Y_i-\bar{Y})^2 \]

This means that we need to subtract the grand mean from each individual data point, square the difference, and sum up over all the squared differences. Thus, in our example, the total sum of squares can be calculated as:

\[ \begin{align} SS_T =&(13−24.67)^2 + (14−24.67)^2 + … + (2−24.67)^2\\ &+(21−24.67)^2 + (18-24.67)^2 + … + (17−24.67)^2\\ &+(30−24.67)^2 + (37−24.67)^2 + … + (28−24.67)^2\\ &=30855.64 \end{align} \]

You could also compute this in R using:

For the subsequent analyses, it is important to understand the concept behind the degrees of freedom . Remember that in order to estimate a population value from a sample, we need to hold something in the population constant. In ANOVA, the df are generally one less than the number of values used to calculate the SS. For example, when we estimate the population mean from a sample, we assume that the sample mean is equal to the population mean. Then, in order to estimate the population mean from the sample, all but one scores are free to vary and the remaining score needs to be the value that keeps the population mean constant. In our example, we used all 300 observations to calculate the sum of square, so the total degrees of freedom (df T ) are:

\[\begin{equation} \begin{split} df_T = N-1=300-1=299 \end{split} \tag{5.1} \end{equation}\]

5.4.2.2 Model sum of squares

Now we know that there are 26646.33 units of total variation in our data. Next, we compute how much of the total variation can be explained by the differences between groups (i.e., our experimental manipulation). To compute the explained variation in the data, we consider the difference between the values predicted by our model for each observation (i.e., the group mean) and the grand mean. The group mean refers to the mean value within the experimental group. The vertical lines in the following plot measure how far the predicted value for each observation (i.e., the group mean) is away from the grand mean:

Figure 5.5: Model Sum of Squares

The formal representation of the model sum of squares (SS M ) is:

\[ SS_M= \sum_{j=1}^{c} n_j(\bar{Y}_j-\bar{Y})^2 \]

where c denotes the number of categories (experimental groups). This means that we need to subtract the grand mean from each group mean, square the difference, and sum up over all the squared differences. Thus, in our example, the model sum of squares can be calculated as:

\[ \begin{align} SS_M &= 100*(15.47−24.67)^2 + 100*(24.88−24.67)^2 + 100*(33.66−24.67)^2 \\ &= 21321.21 \end{align} \]

You could also compute this manually in R using:

In this case, we used the three group means to calculate the sum of squares, so the model degrees of freedom (df M ) are:

\[ df_M= c-1=3-1=2 \]

5.4.2.3 Residual sum of squares

Lastly, we calculate the amount of variation that cannot be explained by our model. In ANOVA, this is the sum of squared distances between what the model predicts for each data point (i.e., the group means) and the observed values. In other words, this refers to the amount of variation that is caused by extraneous factors, such as differences between product characteristics of the products in the different experimental groups. The vertical lines in the following plot measure how far each observation is away from the group mean:

Figure 5.6: Residual Sum of Squares

The formal representation of the residual sum of squares (SS R ) is:

\[ SS_R= \sum_{j=1}^{c} \sum_{i=1}^{n} ({Y}_{ij}-\bar{Y}_{j})^2 \]

This means that we need to subtract the group mean from each individual observation, square the difference, and sum up over all the squared differences. Thus, in our example, the model sum of squares can be calculated as:

\[ \begin{align} SS_R =& (13−14.34)^2 + (14−14.34)^2 + … + (2−14.34)^2 \\ +&(21−24.7)^2 + (18−24.7)^2 + … + (17−24.7)^2 \\ +& (30−34.99)^2 + (37−34.99)^2 + … + (28−34.99)^2 \\ =& 9534.43 \end{align} \]

In this case, we used the 10 values for each of the SS for each group, so the residual degrees of freedom (df R ) are:

\[ \begin{align} df_R=& (n_1-1)+(n_2-1)+(n_3-1) \\ =&(100-1)+(100-1)+(100-1)=297 \end{align} \]

5.4.2.4 Effect strength

Once you have computed the different sum of squares, you can investigate the effect strength. \(\eta^2\) is a measure of the variation in Y that is explained by X:

\[ \eta^2= \frac{SS_M}{SS_T}=\frac{21321.21}{30855.64}=0.69 \]

To compute this in R:

The statistic can only take values between 0 and 1. It is equal to 0 when all the category means are equal, indicating that X has no effect on Y. In contrast, it has a value of 1 when there is no variability within each category of X but there is some variability between categories.

5.4.2.5 Test of significance

How can we determine whether the effect of X on Y is significant?

• First, we calculate the fit of the most basic model (i.e., the grand mean)
• Then, we calculate the fit of the “best” model (i.e., the group means)
• A good model should fit the data significantly better than the basic model
• The F-statistic or F-ratio compares the amount of systematic variance in the data to the amount of unsystematic variance

The F-statistic uses the ratio of mean square related to X (explained variation) and the mean square related to the error (unexplained variation):

\(\frac{SS_M}{SS_R}\)

However, since these are summed values, their magnitude is influenced by the number of scores that were summed. For example, to calculate SS M we only used the sum of 3 values (the group means), while we used 30 and 27 values to calculate SS T and SS R , respectively. Thus, we calculate the average sum of squares (“mean square”) to compare the average amount of systematic vs. unsystematic variation by dividing the SS values by the degrees of freedom associated with the respective statistic.

Mean square due to X:

\[ MS_M= \frac{SS_M}{df_M}=\frac{SS_M}{c-1}=\frac{21321.21}{(3-1)} \]

Mean square due to error:

\[ MS_R= \frac{SS_R}{df_R}=\frac{SS_R}{N-c}=\frac{9534.43}{(300-3)} \]

Now, we compare the amount of variability explained by the model (experiment), to the error in the model (variation due to extraneous variables). If the model explains more variability than it can’t explain, then the experimental manipulation has had a significant effect on the outcome (DV). The F-radio can be derived as follows:

\[ F= \frac{MS_M}{MS_R}=\frac{\frac{SS_M}{c-1}}{\frac{SS_R}{N-c}}=\frac{\frac{21321.21}{(3-1)}}{\frac{9534.43}{(300-3)}}=332.08 \]

You can easily compute this in R:

This statistic follows the F distribution with (m = c – 1) and (n = N – c) degrees of freedom. This means that, like the \(\chi^2\) distribution, the shape of the F-distribution depends on the degrees of freedom. In this case, the shape depends on the degrees of freedom associated with the numerator and denominator used to compute the F-ratio. The following figure shows the shape of the F-distribution for different degrees of freedom:

The F distribution

The outcome of the test is one of the following:

• If the null hypothesis of equal category means is not rejected, then the independent variable does not have a significant effect on the dependent variable
• If the null hypothesis is rejected, then the effect of the independent variable is significant

For 2 and 297 degrees of freedom, the critical value of F is 3.026 for α=0.05. As usual, you can either look up these values in a table or use the appropriate function in R:

The output tells us that the calculated test statistic exceeds the critical value. We can also show the test result visually:

Visual depiction of the test result

Thus, we conclude that because F CAL = 332.08 > F CR = 3.03, H 0 is rejected!

Interpretation: one or more of the differences between means are statistically significant.

Reporting: There was a significant effect of promotion on sales levels, F(2,297) = 332.08, p < 0.05, \(\eta^2\) = 0.69.

Remember: This doesn’t tell us where the differences between groups lie. To find out which group means exactly differ, we need to use post-hoc procedures (see below).

You don’t have to compute these statistics manually! Luckily, there is a function for ANOVA in R, which does the above calculations for you as we will see in the next section.

5.4.3 One-way ANOVA

5.4.3.1 basic anova.

As already indicated, one-way ANOVA is used when there is only one categorical variable (factor). Before conducting ANOVA, you need to check if the assumptions of the test are fulfilled. The assumptions of ANOVA are discussed in the following sections.

Independence of observations

The observations in the groups should be independent. Because we randomly assigned the listeners to the experimental conditions, this assumption can be assumed to be met.

Distributional assumptions

ANOVA is relatively immune to violations to the normality assumption when sample sizes are large due to the Central Limit Theorem. However, if your sample is small (i.e., n < 30 per group) you may nevertheless want to check the normality of your data, e.g., by using the Shapiro-Wilk test or QQ-Plot. In our example, we have 100 observations in each group which is plenty but let’s create another example with only 10 observations in each group. In the latter case we cannot rely on the Central Limit Theorem and we should test the normality of our data. This can be done using the Shapiro-Wilk Test, which has the Null Hypothesis that the data is normally distributed. Hence, an insignificant test results means that the data can be assumed to be approximately normally distributed:

Since the test result is insignificant for all groups, we can conclude that the data approximately follow a normal distribution.

We could also test the distributional assumptions visually using a Q-Q plot (i.e., quantile-quantile plot). This plot can be used to assess if a set of data plausibly came from some theoretical distribution such as the Normal distribution. Since this is just a visual check, it is somewhat subjective. But it may help us to judge if our assumption is plausible, and if not, which data points contribute to the violation. A Q-Q plot is a scatterplot created by plotting two sets of quantiles against one another. If both sets of quantiles came from the same distribution, we should see the points forming a line that’s roughly straight. In other words, Q-Q plots take your sample data, sort it in ascending order, and then plot them versus quantiles calculated from a theoretical distribution. Quantiles are often referred to as “percentiles” and refer to the points in your data below which a certain proportion of your data fall. Recall, for example, the standard Normal distribution with a mean of 0 and a standard deviation of 1. Since the 50th percentile (or 0.5 quantile) is 0, half the data lie below 0. The 95th percentile (or 0.95 quantile), is about 1.64, which means that 95 percent of the data lie below 1.64. The 97.5th quantile is about 1.96, which means that 97.5% of the data lie below 1.96. In the Q-Q plot, the number of quantiles is selected to match the size of your sample data.

To create the Q-Q plot for the normal distribution, you may use the qqnorm() function, which takes the data to be tested as an argument. Using the qqline() function subsequently on the data creates the line on which the data points should fall based on the theoretical quantiles. If the individual data points deviate a lot from this line, it means that the data is not likely to follow a normal distribution.

Figure 5.7: Q-Q plot 1

Figure 5.8: Q-Q plot 2

Figure 5.9: Q-Q plot 3

The Q-Q plots suggest an approximately Normal distribution. If the assumption had been violated, you might consider transforming your data or resort to a non-parametric test.

Homogeneity of variance

Let’s return to our original dataset with 100 observations in each group for the rest of the analysis.

You can test the homogeneity of variances in R using Levene’s test:

The null hypothesis of the test is that the group variances are equal. Thus, if the test result is significant it means that the variances are not equal. If we cannot reject the null hypothesis (i.e., the group variances are not significantly different), we can proceed with the ANOVA as follows:

You can see that the p-value is smaller than 0.05. This means that, if there really was no difference between the population means (i.e., the Null hypothesis was true), the probability of the observed differences (or larger differences) is less than 5%.

To compute η 2 from the output, we can extract the relevant sum of squares as follows

You can see that the results match the results from our manual computation above ( \(\eta^2 =\) 0.69).

The aov() function also automatically generates some plots that you can use to judge if the model assumptions are met. We will inspect two of the plots here.

We will use the first plot to inspect if the residual variances are equal across the experimental groups:

Generally, the residual variance (i.e., the range of values on the y-axis) should be the same for different levels of our independent variable. The plot shows, that there are some slight differences. Notably, the range of residuals is higher in group “B” than in group “C”. However, the differences are not that large and since the Levene’s test could not reject the Null of equal variances, we conclude that the variances are similar enough in this case.

The second plot can be used to test the assumption that the residuals are approximately normally distributed. We use a Q-Q plot to test this assumption:

The plot suggests that, the residuals are approximately normally distributed. We could also test this by extracting the residuals from the anova output using the resid() function and using the Shapiro-Wilk test:

Confirming the impression from the Q-Q plot, we cannot reject the Null that the residuals are approximately normally distributed.

Note that if Levene’s test would have been significant (i.e., variances are not equal), we would have needed to either resort to non-parametric tests (see below), or compute the Welch’s F-ratio instead:

You can see that the results are fairly similar, since the variances turned out to be fairly equal across groups.

5.4.3.2 Post-hoc tests

Provided that significant differences were detected by the overall ANOVA you can find out which group means are different using post hoc procedures. Post hoc procedures are designed to conduct pairwise comparisons of all different combinations of the treatment groups by correcting the level of significance for each test such that the overall Type I error rate (α) across all comparisons remains at 0.05.

In other words, we rejected H 0 : μ 1 = μ 2 = μ 3 , and now we would like to test:

\[H_0: \mu_1 = \mu_2\]

\[H_0: \mu_1 = \mu_3\]

\[H_0: \mu_2 = \mu_3\]

There are several post hoc procedures available to choose from. In this tutorial, we will cover Bonferroni and Tukey’s HSD (“honest significant differences”). Both tests control for family-wise error. Bonferroni tends to have more power when the number of comparisons is small, whereas Tukey’ HSDs is better when testing large numbers of means.

5.4.3.2.1 Bonferroni

One of the most popular (and easiest) methods to correct for the family-wise error rate is to conduct the individual t-tests and divide α by the number of comparisons („k“):

\[ p_{CR}= \frac{\alpha}{k} \]

In our example with three groups:

\[p_{CR}= \frac{0.05}{3}=0.017\]

Thus, the “corrected” critical p-value is now 0.017 instead of 0.05 (i.e., the critical t value is higher). You can implement the Bonferroni procedure in R using:

In the output, you will get the corrected p-values for the individual tests. In our example, we can reject H 0 of equal means for all three tests, since p < 0.05 for all combinations of groups.

Note the difference between the results from the post-hoc test compared to individual t-tests. For example, when we test the “B” vs. “C” groups, the result from a t-test would be:

Usually the p-value is lower in the t-test, reflecting the fact that the family-wise error is not corrected (i.e., the test is less conservative). In this case the p-value is extremely small in both cases and thus indistinguishable.

5.4.3.2.2 Tukey’s HSD

Tukey’s HSD also compares all possible pairs of means (two-by-two combinations; i.e., like a t-test, except that it corrects for family-wise error rate).

Test statistic:

\[\begin{equation} \begin{split} HSD= q\sqrt{\frac{MS_R}{n_c}} \end{split} \tag{5.2} \end{equation}\]

• q = value from studentized range table (see e.g., here )
• MS R = Mean Square Error from ANOVA
• n c = number of observations per group
• Decision: Reject H 0 if

\[|\bar{Y}_i-\bar{Y}_j | > HSD\]

The value from the studentized range table can be obtained using the qtukey() function.

\[HSD= 3.33\sqrt{\frac{33.99}{100}}=1.94\]

Since all mean differences between groups are larger than 1.906, we can reject the null hypothesis for all individual tests, confirming the results from the Bonferroni test. To compute Tukey’s HSD, we can use the appropriate function from the multcomp package.

We may also plot the result for the mean differences incl. their confidence intervals:

Figure 5.10: Tukey’s HSD

You can see that the CIs do not cross zero, which means that the true difference between group means is unlikely zero.

Reporting of post hoc results:

The post hoc tests based on Bonferroni and Tukey’s HSD revealed that people listened to music significantly more when:

• they had access to user created playlists vs. those who did not,
• they got recommendations vs. those who did not. This is true for both the control group “A” as well as treatment “B”.

The following video summarizes how to conduct a one-way ANOVA in R

5.5 Non-parametric tests

Non-Parametric tests do not require the sampling distribution to be normally distributed (a.k.a. “assumption free tests”). These tests may be used when the variable of interest is measured on an ordinal scale or when the parametric assumptions do not hold. They often rely on ranking the data instead of analyzing the actual scores. By ranking the data, information on the magnitude of differences is lost. Thus, parametric tests are more powerful if the sampling distribution is normally distributed.

When should you use non-parametric tests?

• When your DV is measured on an ordinal scale
• When your data is better represented by the median (e.g., there are outliers that you can’t remove)
• When the assumptions of parametric tests are not met (e.g., normally distributed sampling distribution)
• You have a very small sample size (i.e., the central limit theorem does not apply)

5.5.1 Mann-Whitney U Test (a.k.a. Wilcoxon rank-sum test)

The Mann-Whitney U test is a non-parametric test of differences between groups, similar to the two sample t-test. In contrast to the two sample t-test it only requires ordinally scaled data and relies on weaker assumptions. Thus it is often useful if the assumptions of the t-test are violated, especially if the data is not on a ratio scale. The following assumptions must be fulfilled for the test to be applicable:

• The dependent variable is at least ordinally scaled (i.e. a ranking between values can be established)
• The independent variable has only two levels
• A between-subjects design is used (i.e., the subjects are not matched across conditions)

Intuitively, the test compares the frequency of low and high ranks between groups. Under the null hypothesis, the amount of high and low ranks should be roughly equal in the two groups. This is achieved through comparing the expected sum of ranks to the actual sum of ranks.

As an example, we will be using data obtained from a field experiment with random assignment. In a music download store, new releases were randomly assigned to an experimental group and sold at a reduced price (i.e., 7.95€), or a control group and sold at the standard price (9.95€). A representative sample of 102 new releases were sampled and these albums were randomly assigned to the experimental groups (i.e., 51 albums per group). The sales were tracked over one day.

Let’s load and investigate the data first:

Inspect descriptives (overall and by group).

Create boxplot and plot of means.

Figure 5.11: Boxplot

Let’s assume that one of the parametric assumptions has been violated and we needed to conduct a non-parametric test. Then, the Mann-Whitney U test is implemented in R using the function wilcox.test() . Using the ranking data as an independent variable and the listening time as a dependent variable, the test could be executed as follows:

The p-value is smaller than 0.05, which leads us to reject the null hypothesis, i.e. the test yields evidence that the new service feature leads to higher music listening times.

5.5.2 Wilcoxon signed-rank test

The Wilcoxon signed-rank test is a non-parametric test used to analyze the difference between paired observations, analogously to the paired t-test. It can be used when measurements come from the same observational units but the distributional assumptions of the paired t-test do not hold, because it does not require any assumptions about the distribution of the measurements. Since we subtract two values, however, the test requires that the dependent variable is at least interval scaled, meaning that intervals have the same meaning for different points on our measurement scale.

Under the null hypothesis \(H_0\) , the differences of the measurements should follow a symmetric distribution around 0, meaning that, on average, there is no difference between the two matched samples. \(H_1\) states that the distributions mean is non-zero.

As an example, let’s consider a slightly different experimental setup for the music download store. Imagine that new releases were either sold at a reduced price (i.e., 7.95€), or at the standard price (9.95€). Every time a customer came to the store, the prices were randomly determined for every new release. This means that the same 51 albums were either sold at the standard price or at the reduced price and this price was determined randomly. The sales were then recorded over one day. Note the difference to the previous case, where we randomly split the sample and assigned 50% of products to each condition. Now, we randomly vary prices for all albums between high and low prices.

Again, let’s assume that one of the prarametric assumptions has been violated and we needed to conduct a non-parametric test. Then the Wilcoxon signed-rank test can be performed with the same command as the Mann-Whitney U test, provided that the argument paired is set to TRUE .

Using the 95% confidence level, the result would suggest a significant effect of price on sales (i.e., p < 0.05).

5.5.3 Kruskal-Wallis test

• When the dependent variable is measured at an ordinal scale and we want to compare more than 2 means
• When the assumptions of independent ANOVA are not met (e.g., assumptions regarding the sampling distribution in small samples)

The Kruskal–Wallis test is the non-parametric counterpart of the one-way independent ANOVA. It is designed to test for significant differences in population medians when you have more than two samples (otherwise you would use the Mann-Whitney U-test). The theory is very similar to that of the Mann–Whitney U-test since it is also based on ranked data. The Kruskal-Wallis test is carried out using the kruskal.test() function. Using the same data as before, we type:

The test-statistic follows a chi-square distribution and since the test is significant (p < 0.05), we can conclude that there are significant differences in population medians. Provided that the overall effect is significant, you may perform a post hoc test to find out which groups are different. To get a first impression, we can plot the data using a boxplot:

Figure 5.12: Boxplot

To test for differences between groups, we can, for example, apply post hoc tests according to Nemenyi for pairwise multiple comparisons of the ranked data using the appropriate function from the PMCMR package.

The results reveal that there is a significant difference between the “low” and “high” promotion groups. Note that the results are different compared to the results from the parametric test above. This difference occurs because non-parametric tests have more power to detect differences between groups since we lose information by ranking the data. Thus, you should rely on parametric tests if the assumptions are met.

5.6 Categorical data

In some instances, you will be confronted with differences between proportions, rather than differences between means. For example, you may conduct an A/B-Test and wish to compare the conversion rates between two advertising campaigns. In this case, your data is binary (0 = no conversion, 1 = conversion) and the sampling distribution for such data is binomial. While binomial probabilities are difficult to calculate, we can use a Normal approximation to the binomial when n is large (>100) and the true likelihood of a 1 is not too close to 0 or 1.

Let’s use an example: assume a call center where service agents call potential customers to sell a product. We consider two call center agents:

• Service agent 1 talks to 300 customers and gets 200 of them to buy (conversion rate=2/3)
• Service agent 2 talks to 300 customers and gets 100 of them to buy (conversion rate=1/3)

As always, we load the data first:

Next, we create a table to check the relative frequencies:

We could also plot the data to visualize the frequencies using ggplot:

Figure 5.13: proportion of conversions per agent (stacked bar chart)

… or using the mosaicplot() function:

Figure 5.14: proportion of conversions per agent (mosaic plot)

5.6.1 Confidence intervals for proportions

Recall that we can use confidence intervals to determine the range of values that the true population parameter will take with a certain level of confidence based on the sample. Similar to the confidence interval for means, we can compute a confidence interval for proportions. The (1- \(\alpha\) )% confidence interval for proportions is approximately

\[ CI = p\pm z_{1-\frac{\alpha}{2}}*\sqrt{\frac{p*(1-p)}{N}} \]

where \(\sqrt{p(1-p)}\) is the equivalent to the standard deviation in the formula for the confidence interval for means. Based on the equation, it is easy to compute the confidence intervals for the conversion rates of the call center agents:

Similar to testing for differences in means, we could also ask: Is agent 1 twice as likely as agent 2 to convert a customer? Or, to state it formally:

\[H_0: \pi_1=\pi_2 \\ H_1: \pi_1\ne \pi_2\]

where \(\pi\) denotes the population parameter associated with the proportion in the respective population. One approach to test this is based on confidence intervals to estimate the difference between two populations. We can compute an approximate confidence interval for the difference between the proportion of successes in group 1 and group 2, as:

\[ CI = p_1-p_2\pm z_{1-\frac{\alpha}{2}}*\sqrt{\frac{p_1*(1-p_1)}{n_1}+\frac{p_2*(1-p_2)}{n_2}} \]

If the confidence interval includes zero, then the data does not suggest a difference between the groups. Let’s compute the confidence interval for differences in the proportions by hand first:

Now we can see that the 95% confidence interval estimate of the difference between the proportion of conversions for agent 1 and the proportion of conversions for agent 2 is between 26% and 41%. This interval tells us the range of plausible values for the difference between the two population proportions. According to this interval, zero is not a plausible value for the difference (i.e., interval does not cross zero), so we reject the null hypothesis that the population proportions are the same.

Instead of computing the intervals by hand, we could also use the prop.test() function:

Note that the prop.test() function uses a slightly different (more accurate) way to compute the confidence interval (Wilson’s score method is used). It is particularly a better approximation for smaller N. That’s why the confidence interval in the output slightly deviates from the manual computation above, which uses the Wald interval.

You can also see that the output from the prop.test() includes the results from a χ 2 test for the equality of proportions (which will be discussed below) and the associated p-value. Since the p-value is less than 0.05, we reject the null hypothesis of equal probability. Thus, the reporting would be:

The test showed that the conversion rate for agent 1 was higher by 33%. This difference is significant χ (1) = 70, p < .05 (95% CI = [0.25,0.41]).

5.6.2 Chi-square test

In the previous section, we saw how we can compute the confidence interval for the difference between proportions to decide on whether or not to reject the null hypothesis. Whenever you would like to investigate the relationship between two categorical variables, the \(\chi^2\) test may be used to test whether the variables are independent of each other. It achieves this by comparing the expected number of observations in a group to the actual values. Let’s continue with the example from the previous section. Under the null hypothesis, the two variables agent and conversion in our contingency table are independent (i.e., there is no relationship). This means that the frequency in each field will be roughly proportional to the probability of an observation being in that category, calculated under the assumption that they are independent. The difference between that expected quantity and the actual quantity can be used to construct the test statistic. The test statistic is computed as follows:

\[ \chi^2=\sum_{i=1}^{J}\frac{(f_o-f_e)^2}{f_e} \]

where \(J\) is the number of cells in the contingency table, \(f_o\) are the observed cell frequencies and \(f_e\) are the expected cell frequencies. The larger the differences, the larger the test statistic and the smaller the p-value.

The observed cell frequencies can easily be seen from the contingency table:

The expected cell frequencies can be calculated as follows:

\[ f_e=\frac{(n_r*n_c)}{n} \]

where \(n_r\) are the total observed frequencies per row, \(n_c\) are the total observed frequencies per column, and \(n\) is the total number of observations. Thus, the expected cell frequencies under the assumption of independence can be calculated as:

To sum up, these are the expected cell frequencies

… and these are the observed cell frequencies

To obtain the test statistic, we simply plug the values into the formula:

The test statistic is \(\chi^2\) distributed. The chi-square distribution is a non-symmetric distribution. Actually, there are many different chi-square distributions, one for each degree of freedom as show in the following figure.

Figure 5.15: The chi-square distribution

You can see that as the degrees of freedom increase, the chi-square curve approaches a normal distribution. To find the critical value, we need to specify the corresponding degrees of freedom, given by:

\[ df=(r-1)*(c-1) \]

where \(r\) is the number of rows and \(c\) is the number of columns in the contingency table. Recall that degrees of freedom are generally the number of values that can vary freely when calculating a statistic. In a 2 by 2 table as in our case, we have 2 variables (or two samples) with 2 levels and in each one we have 1 that vary freely. Hence, in our example the degrees of freedom can be calculated as:

Now, we can derive the critical value given the degrees of freedom and the level of confidence using the qchisq() function and test if the calculated test statistic is larger than the critical value:

Figure 5.16: Visual depiction of the test result

We could also compute the p-value using the pchisq() function, which tells us the probability of the observed cell frequencies if the null hypothesis was true (i.e., there was no association):

The test statistic can also be calculated in R directly on the contingency table with the function chisq.test() .

Since the p-value is smaller than 0.05 (i.e., the calculated test statistic is larger than the critical value), we reject H 0 that the two variables are independent.

Note that the test statistic is sensitive to the sample size. To see this, let’s assume that we have a sample of 100 observations instead of 1000 observations:

You can see that even though the proportions haven’t changed, the test is insignificant now. The following equation lets you compute a measure of the effect size, which is insensitive to sample size:

\[ \phi=\sqrt{\frac{\chi^2}{n}} \]

The following guidelines are used to determine the magnitude of the effect size (Cohen, 1988):

• 0.1 (small effect)
• 0.3 (medium effect)
• 0.5 (large effect)

In our example, we can compute the effect sizes for the large and small samples as follows:

You can see that the statistic is insensitive to the sample size.

Note that the Φ coefficient is appropriate for two dichotomous variables (resulting from a 2 x 2 table as above). If any your nominal variables has more than two categories, Cramér’s V should be used instead:

\[ V=\sqrt{\frac{\chi^2}{n*df_{min}}} \]

where \(df_{min}\) refers to the degrees of freedom associated with the variable that has fewer categories (e.g., if we have two nominal variables with 3 and 4 categories, \(df_{min}\) would be 3 - 1 = 2). The degrees of freedom need to be taken into account when judging the magnitude of the effect sizes (see e.g., here ).

Note that the correct = FALSE argument above ensures that the test statistic is computed in the same way as we have done by hand above. By default, chisq.test() applies a correction to prevent overestimation of statistical significance for small data (called the Yates’ correction). The correction is implemented by subtracting the value 0.5 from the computed difference between the observed and expected cell counts in the numerator of the test statistic. This means that the calculated test statistic will be smaller (i.e., more conservative). Although the adjustment may go too far in some instances, you should generally rely on the adjusted results, which can be computed as follows:

As you can see, the results don’t change much in our example, since the differences between the observed and expected cell frequencies are fairly large relative to the correction.

Caution is warranted when the cell counts in the contingency table are small. The usual rule of thumb is that all cell counts should be at least 5 (this may be a little too stringent though). When some cell counts are too small, you can use Fisher’s exact test using the fisher.test() function.

The Fisher test, while more conservative, also shows a significant difference between the proportions (p < 0.05). This is not surprising since the cell counts in our example are fairly large.

5.6.3 Sample size

To calculate the required sample size when comparing proportions, the power.prop.test() function can be used. For example, we could ask how large our sample needs to be if we would like to compare two groups with conversion rates of 2% and 2.5%, respectively using the conventional settings for \(\alpha\) and \(\beta\) :

The output tells us that we need 13809 observations per group to detect a difference of the desired size.

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A/B Testing: Example of a good hypothesis

Want to know the secret to always running successful tests?

The answer is to formulate a hypothesis .

Now when I say it’s always successful, I’m not talking about always increasing your Key Performance Indicator (KPI). You can “lose” a test, but still be successful.

That sounds like an oxymoron, but it’s not. If you set up your test strategically, even if the test decreases your KPI, you gain a learning , which is a success! And, if you win, you simultaneously achieve a lift and a learning. Double win!

The way you ensure you have a strategic test that will produce a learning is by centering it around a strong hypothesis.

So, what is a hypothesis?

By definition, a hypothesis is a proposed statement made on the basis of limited evidence that can be proved or disproved and is used as a starting point for further investigation.

Let’s break that down:

It is a proposed statement.

• A hypothesis is not fact, and should not be argued as right or wrong until it is tested and proven one way or the other.

It is made on the basis of limited (but hopefully some ) evidence.

• Your hypothesis should be informed by as much knowledge as you have. This should include data that you have gathered, any research you have done, and the analysis of the current problems you have performed.

It can be proved or disproved.

• A hypothesis pretty much says, “I think by making this change , it will cause this effect .” So, based on your results, you should be able to say “this is true” or “this is false.”

It is used as a starting point for further investigation.

• The key word here is starting point . Your hypothesis should be formed and agreed upon before you make any wireframes or designs as it is what guides the design of your test. It helps you focus on what elements to change, how to change them, and which to leave alone.

How do I write a hypothesis?

The structure of your basic hypothesis follows a CHANGE: EFFECT framework.

While this is a truly scientific and testable template, it is very open-ended. Even though this hypothesis, “Changing an English headline into a Spanish headline will increase clickthrough rate,” is perfectly valid and testable, if your visitors are English-speaking, it probably doesn’t make much sense.

So now the question is …

How do I write a GOOD hypothesis?

To quote my boss Tony Doty , “This isn’t Mad Libs.”

We can’t just start plugging in nouns and verbs and conclude that we have a good hypothesis. Your hypothesis needs to be backed by a strategy. And, your strategy needs to be rooted in a solution to a problem .

So, a more complete version of the above template would be something like this:

In order to have a good hypothesis, you don’t necessarily have to follow this exact sentence structure, as long as it is centered around three main things:

Presumed problem

Proposed solution

Anticipated result

After you’ve completed your analysis and research, identify the problem that you will address. While we need to be very clear about what we think the problem is, you should leave it out of the hypothesis since it is harder to prove or disprove. You may want to come up with both a problem statement and a hypothesis .

For example:

Problem Statement: “The lead generation form is too long, causing unnecessary friction .”

Hypothesis: “By changing the amount of form fields from 20 to 10, we will increase number of leads.”

When you are thinking about the solution you want to implement, you need to think about the psychology of the customer. What psychological impact is your proposed problem causing in the mind of the customer?

For example, if your proposed problem is “There is a lack of clarity in the sign-up process,” the psychological impact may be that the user is confused.

Now think about what solution is going to address the problem in the customer’s mind. If they are confused, we need to explain something better, or provide them with more information. For this example, we will say our proposed solution is to “Add a progress bar to the sign-up process.”  This leads straight into the anticipated result.

If we reduce the confusion in the visitor’s mind (psychological impact) by adding the progress bar, what do we foresee to be the result? We are anticipating that it would be more people completing the sign-up process. Your proposed solution and your KPI need to be directly correlated.

Note: Some people will include the psychological impact in their hypothesis. This isn’t necessarily wrong, but we do have to be careful with assumptions. If we say that the effect will be “Reduced confusion and therefore increase in conversion rate,” we are assuming the reduced confusion is what made the impact. While this may be correct, it is not measureable and it is hard to prove or disprove.

To summarize, your hypothesis should follow a structure of: “If I change this, it will have this effect,” but should always be informed by an analysis of the problems and rooted in the solution you deemed appropriate.

Related Resources:

A/B Testing 101: How to get real results from optimization

The True Value of Data

15 Years of Marketing Research in 11 Minutes

Marketing Analytics: 6 simple steps for interpreting your data

Website A/B Testing: 4 tips to beat an unbeatable landing page

Online Cart: 6 ideas to test and optimize your checkout process

B2B Gamification: Autodesk’s two approaches to in-trial marketing [Video]

How to Discover Exactly What the Customer Wants to See on the Next Click: 3 critical…

The 21 Psychological Elements that Power Effective Web Design (Part 3)

The 21 Psychological Elements that Power Effective Web Design (Part 2)

The 21 Psychological Elements that Power Effective Web Design (Part 1)

Thanks for the article. I’ve been trying to wrap my head around this type of testing because I’d like to use it to see the effectiveness on some ads. This article really helped. Thanks Again!

Hey Lauren, I am just getting to the point that I have something to perform A-B testing on. This post led me to this site which will and already has become a help in what to test and how to test .

Again, thanks for getting me here .

Good article. I have been researching different approaches to writing testing hypotheses and this has been a help. The only thing I would add is that it can be useful to capture the insight/justification within the hypothesis statement. IF i do this, THEN I expect this result BECAUSE I have this insight.

@Kaya Great!

Good article – but technically you can never prove an hypothesis, according to the principle of falsification (Popper), only fail to disprove the null hypothesis.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

How to Generate and Validate Product Hypotheses

Every product owner knows that it takes effort to build something that'll cater to user needs. You'll have to make many tough calls if you wish to grow the company and evolve the product so it delivers more value. But how do you decide what to change in the product, your marketing strategy, or the overall direction to succeed? And how do you make a product that truly resonates with your target audience?

There are many unknowns in business, so many fundamental decisions start from a simple "what if?". But they can't be based on guesses, as you need some proof to fill in the blanks reasonably.

Because there's no universal recipe for successfully building a product, teams collect data, do research, study the dynamics, and generate hypotheses according to the given facts. They then take corresponding actions to find out whether they were right or wrong, make conclusions, and most likely restart the process again.

On this page, we thoroughly inspect product hypotheses. We'll go over what they are, how to create hypothesis statements and validate them, and what goes after this step.

What Is a Hypothesis in Product Management?

A hypothesis in product development and product management is a statement or assumption about the product, planned feature, market, or customer (e.g., their needs, behavior, or expectations) that you can put to the test, evaluate, and base your further decisions on . This may, for instance, regard the upcoming product changes as well as the impact they can result in.

A hypothesis implies that there is limited knowledge. Hence, the teams need to undergo testing activities to validate their ideas and confirm whether they are true or false.

Hypotheses guide the product development process and may point at important findings to help build a better product that'll serve user needs. In essence, teams create hypothesis statements in an attempt to improve the offering, boost engagement, increase revenue, find product-market fit quicker, or for other business-related reasons.

It's sort of like an experiment with trial and error, yet, it is data-driven and should be unbiased . This means that teams don't make assumptions out of the blue. Instead, they turn to the collected data, conducted market research , and factual information, which helps avoid completely missing the mark. The obtained results are then carefully analyzed and may influence decision-making.

Such experiments backed by data and analysis are an integral aspect of successful product development and allow startups or businesses to dodge costly startup mistakes .

‍ When do teams create hypothesis statements and validate them? To some extent, hypothesis testing is an ongoing process to work on constantly. It may occur during various product development life cycle stages, from early phases like initiation to late ones like scaling.

In any event, the key here is learning how to generate hypothesis statements and validate them effectively. We'll go over this in more detail later on.

Idea vs. Hypothesis Compared

You might be wondering whether ideas and hypotheses are the same thing. Well, there are a few distinctions.

An idea is simply a suggested proposal. Say, a teammate comes up with something you can bring to life during a brainstorming session or pitches in a suggestion like "How about we shorten the checkout process?". You can jot down such ideas and then consider working on them if they'll truly make a difference and improve the product, strategy, or result in other business benefits. Ideas may thus be used as the hypothesis foundation when you decide to prove a concept.

A hypothesis is the next step, when an idea gets wrapped with specifics to become an assumption that may be tested. As such, you can refine the idea by adding details to it. The previously mentioned idea can be worded into a product hypothesis statement like: "The cart abandonment rate is high, and many users flee at checkout. But if we shorten the checkout process by cutting down the number of steps to only two and get rid of four excessive fields, we'll simplify the user journey, boost satisfaction, and may get up to 15% more completed orders".

A hypothesis is something you can test in an attempt to reach a certain goal. Testing isn't obligatory in this scenario, of course, but the idea may be tested if you weigh the pros and cons and decide that the required effort is worth a try. We'll explain how to create hypothesis statements next.

How to Generate a Hypothesis for a Product

The last thing those developing a product want is to invest time and effort into something that won't bring any visible results, fall short of customer expectations, or won't live up to their needs. Therefore, to increase the chances of achieving a successful outcome and product-led growth , teams may need to revisit their product development approach by optimizing one of the starting points of the process: learning to make reasonable product hypotheses.

If the entire procedure is structured, this may assist you during such stages as the discovery phase and raise the odds of reaching your product goals and setting your business up for success. Yet, what's the entire process like?

• It all starts with identifying an existing problem . Is there a product area that's experiencing a downfall, a visible trend, or a market gap? Are users often complaining about something in their feedback? Or is there something you're willing to change (say, if you aim to get more profit, increase engagement, optimize a process, expand to a new market, or reach your OKRs and KPIs faster)?
• Teams then need to work on formulating a hypothesis . They put the statement into concise and short wording that describes what is expected to achieve. Importantly, it has to be relevant, actionable, backed by data, and without generalizations.
• Next, they have to test the hypothesis by running experiments to validate it (for instance, via A/B or multivariate testing, prototyping, feedback collection, or other ways).
• Then, the obtained results of the test must be analyzed . Did one element or page version outperform the other? Depending on what you're testing, you can look into various merits or product performance metrics (such as the click rate, bounce rate, or the number of sign-ups) to assess whether your prediction was correct.
• Finally, the teams can make conclusions that could lead to data-driven decisions. For example, they can make corresponding changes or roll back a step.

How Else Can You Generate Product Hypotheses?

Such processes imply sharing ideas when a problem is spotted by digging deep into facts and studying the possible risks, goals, benefits, and outcomes. You may apply various MVP tools like (FigJam, Notion, or Miro) that were designed to simplify brainstorming sessions, systemize pitched suggestions, and keep everyone organized without losing any ideas.

Predictive product analysis can also be integrated into this process, leveraging data and insights to anticipate market trends and consumer preferences, thus enhancing decision-making and product development strategies. This approach fosters a more proactive and informed approach to innovation, ensuring products are not only relevant but also resonate with the target audience, ultimately increasing their chances of success in the market.

Besides, you can settle on one of the many frameworks that facilitate decision-making processes , ideation phases, or feature prioritization . Such frameworks are best applicable if you need to test your assumptions and structure the validation process. These are a few common ones if you're looking toward a systematic approach:

• Business Model Canvas (used to establish the foundation of the business model and helps find answers to vitals like your value proposition, finding the right customer segment, or the ways to make revenue);
• Lean Startup framework (the lean startup framework uses a diagram-like format for capturing major processes and can be handy for testing various hypotheses like how much value a product brings or assumptions on personas, the problem, growth, etc.);
• Design Thinking Process (is all about interactive learning and involves getting an in-depth understanding of the customer needs and pain points, which can be formulated into hypotheses followed by simple prototypes and tests).

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How to Make a Hypothesis Statement for a Product

Once you've indicated the addressable problem or opportunity and broken down the issue in focus, you need to work on formulating the hypotheses and associated tasks. By the way, it works the same way if you want to prove that something will be false (a.k.a null hypothesis).

If you're unsure how to write a hypothesis statement, let's explore the essential steps that'll set you on the right track.

Step 1: Allocate the Variable Components

Product hypotheses are generally different for each case, so begin by pinpointing the major variables, i.e., the cause and effect . You'll need to outline what you think is supposed to happen if a change or action gets implemented.

Put simply, the "cause" is what you're planning to change, and the "effect" is what will indicate whether the change is bringing in the expected results. Falling back on the example we brought up earlier, the ineffective checkout process can be the cause, while the increased percentage of completed orders is the metric that'll show the effect.

Make sure to also note such vital points as:

• what the problem and solution are;
• what are the benefits or the expected impact/successful outcome;
• which user group is affected;
• what are the risks;
• what kind of experiments can help test the hypothesis;
• what can measure whether you were right or wrong.

Step 2: Ensure the Connection Is Specific and Logical

Mind that generic connections that lack specifics will get you nowhere. So if you're thinking about how to word a hypothesis statement, make sure that the cause and effect include clear reasons and a logical dependency .

Think about what can be the precise and link showing why A affects B. In our checkout example, it could be: fewer steps in the checkout and the removed excessive fields will speed up the process, help avoid confusion, irritate users less, and lead to more completed orders. That's much more explicit than just stating the fact that the checkout needs to be changed to get more completed orders.

Step 3: Decide on the Data You'll Collect

Certainly, multiple things can be used to measure the effect. Therefore, you need to choose the optimal metrics and validation criteria that'll best envision if you're moving in the right direction.

If you need a tip on how to create hypothesis statements that won't result in a waste of time, try to avoid vagueness and be as specific as you can when selecting what can best measure and assess the results of your hypothesis test. The criteria must be measurable and tied to the hypotheses . This can be a realistic percentage or number (say, you expect a 15% increase in completed orders or 2x fewer cart abandonment cases during the checkout phase).

Once again, if you're not realistic, then you might end up misinterpreting the results. Remember that sometimes an increase that's even as little as 2% can make a huge difference, so why make 50% the merit if it's not achievable in the first place?

Step 4: Settle on the Sequence

It's quite common that you'll end up with multiple product hypotheses. Some are more important than others, of course, and some will require more effort and input.

Therefore, just as with the features on your product development roadmap , prioritize your hypotheses according to their impact and importance. Then, group and order them, especially if the results of some hypotheses influence others on your list.

Product Hypothesis Examples

To demonstrate how to formulate your assumptions clearly, here are several more apart from the example of a hypothesis statement given above:

• Adding a wishlist feature to the cart with the possibility to send a gift hint to friends via email will increase the likelihood of making a sale and bring in additional sign-ups.
• Placing a limited-time promo code banner stripe on the home page will increase the number of sales in March.
• Moving up the call to action element on the landing page and changing the button text will increase the click-through rate twice.
• By highlighting a new way to use the product, we'll target a niche customer segment (i.e., single parents under 30) and acquire 5% more leads. 

How to Validate Hypothesis Statements: The Process Explained

There are multiple options when it comes to validating hypothesis statements. To get appropriate results, you have to come up with the right experiment that'll help you test the hypothesis. You'll need a control group or people who represent your target audience segments or groups to participate (otherwise, your results might not be accurate).

‍ What can serve as the experiment you may run? Experiments may take tons of different forms, and you'll need to choose the one that clicks best with your hypothesis goals (and your available resources, of course). The same goes for how long you'll have to carry out the test (say, a time period of two months or as little as two weeks). Here are several to get you started.

Feedback and User Testing

Talking to users, potential customers, or members of your own online startup community can be another way to test your hypotheses. You may use surveys, questionnaires, or opt for more extensive interviews to validate hypothesis statements and find out what people think. This assumption validation approach involves your existing or potential users and might require some additional time, but can bring you many insights.

Conduct A/B or Multivariate Tests

One of the experiments you may develop involves making more than one version of an element or page to see which option resonates with the users more. As such, you can have a call to action block with different wording or play around with the colors, imagery, visuals, and other things.

To run such split experiments, you can apply tools like VWO that allows to easily construct alternative designs and split what your users see (e.g., one half of the users will see version one, while the other half will see version two). You can track various metrics and apply heatmaps, click maps, and screen recordings to learn more about user response and behavior. Mind, though, that the key to such tests is to get as many users as you can give the tests time. Don't jump to conclusions too soon or if very few people participated in your experiment.

Build Prototypes and Fake Doors

Demos and clickable prototypes can be a great way to save time and money on costly feature or product development. A prototype also allows you to refine the design. However, they can also serve as experiments for validating hypotheses, collecting data, and getting feedback.

For instance, if you have a new feature in mind and want to ensure there is interest, you can utilize such MVP types as fake doors . Make a short demo recording of the feature and place it on your landing page to track interest or test how many people sign up.

Usability Testing

Similarly, you can run experiments to observe how users interact with the feature, page, product, etc. Usually, such experiments are held on prototype testing platforms with a focus group representing your target visitors. By showing a prototype or early version of the design to users, you can view how people use the solution, where they face problems, or what they don't understand. This may be very helpful if you have hypotheses regarding redesigns and user experience improvements before you move on from prototype to MVP development.

You can even take it a few steps further and build a barebone feature version that people can really interact with, yet you'll be the one behind the curtain to make it happen. There were many MVP examples when companies applied Wizard of Oz or concierge MVPs to validate their hypotheses.

Or you can actually develop some functionality but release it for only a limited number of people to see. This is referred to as a feature flag , which can show really specific results but is effort-intensive. 

What Comes After Hypothesis Validation?

Analysis is what you move on to once you've run the experiment. This is the time to review the collected data, metrics, and feedback to validate (or invalidate) the hypothesis.

You have to evaluate the experiment's results to determine whether your product hypotheses were valid or not. For example, if you were testing two versions of an element design, color scheme, or copy, look into which one performed best.

It is crucial to be certain that you have enough data to draw conclusions, though, and that it's accurate and unbiased . Because if you don't, this may be a sign that your experiment needs to be run for some additional time, be altered, or held once again. You won't want to make a solid decision based on uncertain or misleading results, right?

• If the hypothesis was supported , proceed to making corresponding changes (such as implementing a new feature, changing the design, rephrasing your copy, etc.). Remember that your aim was to learn and iterate to improve.
• If your hypothesis was proven false , think of it as a valuable learning experience. The main goal is to learn from the results and be able to adjust your processes accordingly. Dig deep to find out what went wrong, look for patterns and things that may have skewed the results. But if all signs show that you were wrong with your hypothesis, accept this outcome as a fact, and move on. This can help you make conclusions on how to better formulate your product hypotheses next time. Don't be too judgemental, though, as a failed experiment might only mean that you need to improve the current hypothesis, revise it, or create a new one based on the results of this experiment, and run the process once more.

On another note, make sure to record your hypotheses and experiment results . Some companies use CRMs to jot down the key findings, while others use something as simple as Google Docs. Either way, this can be your single source of truth that can help you avoid running the same experiments or allow you to compare results over time.

Have doubts about how to bring your product to life?

Upsilon's team of pros can help you build a product most optimally.

Final Thoughts on Product Hypotheses

The hypothesis-driven approach in product development is a great way to avoid uncalled-for risks and pricey mistakes. You can back up your assumptions with facts, observe your target audience's reactions, and be more certain that this move will deliver value.

However, this only makes sense if the validation of hypothesis statements is backed by relevant data that'll allow you to determine whether the hypothesis is valid or not. By doing so, you can be certain that you're developing and testing hypotheses to accelerate your product management and avoiding decisions based on guesswork.

Certainly, a failed experiment may bring you just as much knowledge and findings as one that succeeds. Teams have to learn from their mistakes, boost their hypothesis generation and testing knowledge , and make improvements according to the results of their experiments. This is an ongoing process, of course, as no product can grow if it isn't iterated and improved.

If you're only planning to or are currently building a product, Upsilon can lend you a helping hand. Our team has years of experience providing product development services for growth-stage startups and building MVPs for early-stage businesses , so you can use our expertise and knowledge to dodge many mistakes. Don't be shy to contact us to discuss your needs! 

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9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

Published: August 08, 2024

One of the most underrated skills you can have as a marketer is marketing research — which is great news for this unapologetic cyber sleuth.

From brand design and product development to buyer personas and competitive analysis, I’ve researched a number of initiatives in my decade-long marketing career.

And let me tell you: having the right marketing research methods in your toolbox is a must.

Market research is the secret to crafting a strategy that will truly help you accomplish your goals. The good news is there is no shortage of options.

How to Choose a Marketing Research Method

Thanks to the Internet, we have more marketing research (or market research) methods at our fingertips than ever, but they’re not all created equal. Let’s quickly go over how to choose the right one.

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1. Identify your objective.

What are you researching? Do you need to understand your audience better? How about your competition? Or maybe you want to know more about your customer’s feelings about a specific product.

Before starting your research, take some time to identify precisely what you’re looking for. This could be a goal you want to reach, a problem you need to solve, or a question you need to answer.

For example, an objective may be as foundational as understanding your ideal customer better to create new buyer personas for your marketing agency (pause for flashbacks to my former life).

Or if you’re an organic sode company, it could be trying to learn what flavors people are craving.

2. Determine what type of data and research you need.

Next, determine what data type will best answer the problems or questions you identified. There are primarily two types: qualitative and quantitative. (Sound familiar, right?)

• Qualitative Data is non-numerical information, like subjective characteristics, opinions, and feelings. It’s pretty open to interpretation and descriptive, but it’s also harder to measure. This type of data can be collected through interviews, observations, and open-ended questions.
• Quantitative Data , on the other hand, is numerical information, such as quantities, sizes, amounts, or percentages. It’s measurable and usually pretty hard to argue with, coming from a reputable source. It can be derived through surveys, experiments, or statistical analysis.

Understanding the differences between qualitative and quantitative data will help you pinpoint which research methods will yield the desired results.

For instance, thinking of our earlier examples, qualitative data would usually be best suited for buyer personas, while quantitative data is more useful for the soda flavors.

However, truth be told, the two really work together.

Qualitative conclusions are usually drawn from quantitative, numerical data. So, you’ll likely need both to get the complete picture of your subject.

For example, if your quantitative data says 70% of people are Team Black and only 30% are Team Green — Shout out to my fellow House of the Dragon fans — your qualitative data will say people support Black more than Green.

(As they should.)

Primary Research vs Secondary Research

You’ll also want to understand the difference between primary and secondary research.

Primary research involves collecting new, original data directly from the source (say, your target market). In other words, it’s information gathered first-hand that wasn’t found elsewhere.

Some examples include conducting experiments, surveys, interviews, observations, or focus groups.

Meanwhile, secondary research is the analysis and interpretation of existing data collected from others. Think of this like what we used to do for school projects: We would read a book, scour the internet, or pull insights from others to work from.

So, which is better?

Personally, I say any research is good research, but if you have the time and resources, primary research is hard to top. With it, you don’t have to worry about your source's credibility or how relevant it is to your specific objective.

You are in full control and best equipped to get the reliable information you need.

3. Put it all together.

Once you know your objective and what kind of data you want, you’re ready to select your marketing research method.

For instance, let’s say you’re a restaurant trying to see how attendees felt about the Speed Dating event you hosted last week.

You shouldn’t run a field experiment or download a third-party report on speed dating events; those would be useless to you. You need to conduct a survey that allows you to ask pointed questions about the event.

This would yield both qualitative and quantitative data you can use to improve and bring together more love birds next time around.

Best Market Research Methods for 2024

Now that you know what you’re looking for in a marketing research method, let’s dive into the best options.

Note: According to HubSpot’s 2024 State of Marketing report, understanding customers and their needs is one of the biggest challenges facing marketers today. The options we discuss are great consumer research methodologies , but they can also be used for other areas.

Primary Research

1. interviews.

Interviews are a form of primary research where you ask people specific questions about a topic or theme. They typically deliver qualitative information.

I’ve conducted many interviews for marketing purposes, but I’ve also done many for journalistic purposes, like this profile on comedian Zarna Garg . There’s no better way to gather candid, open-ended insights in my book, but that doesn’t mean they’re a cure-all.

What I like: Real-time conversations allow you to ask different questions if you’re not getting the information you need. They also push interviewees to respond quickly, which can result in more authentic answers.

What I dislike: They can be time-consuming and harder to measure (read: get quantitative data) unless you ask pointed yes or no questions.

Best for: Creating buyer personas or getting feedback on customer experience, a product, or content.

2. Focus Groups

Focus groups are similar to conducting interviews but on a larger scale.

In marketing and business, this typically means getting a small group together in a room (or Zoom), asking them questions about various topics you are researching. You record and/or observe their responses to then take action.

They are ideal for collecting long-form, open-ended feedback, and subjective opinions.

One well-known focus group you may remember was run by Domino’s Pizza in 2009 .

After poor ratings and dropping over $100 million in revenue, the brand conducted focus groups with real customers to learn where they could have done better.

It was met with comments like “worst excuse for pizza I’ve ever had” and “the crust tastes like cardboard.” But rather than running from the tough love, it took the hit and completely overhauled its recipes.

The team admitted their missteps and returned to the market with better food and a campaign detailing their “Pizza Turn Around.”

The result? The brand won a ton of praise for its willingness to take feedback, efforts to do right by its consumers, and clever campaign. But, most importantly, revenue for Domino’s rose by 14.3% over the previous year.

The brand continues to conduct focus groups and share real footage from them in its promotion:

What I like: Similar to interviewing, you can dig deeper and pivot as needed due to the real-time nature. They’re personal and detailed.

What I dislike: Once again, they can be time-consuming and make it difficult to get quantitative data. There is also a chance some participants may overshadow others.

Best for: Product research or development

Pro tip: Need help planning your focus group? Our free Market Research Kit includes a handy template to start organizing your thoughts in addition to a SWOT Analysis Template, Survey Template, Focus Group Template, Presentation Template, Five Forces Industry Analysis Template, and an instructional guide for all of them. Download yours here now.

3. Surveys or Polls

Surveys are a form of primary research where individuals are asked a collection of questions. It can take many different forms.

They could be in person, over the phone or video call, by email, via an online form, or even on social media. Questions can be also open-ended or closed to deliver qualitative or quantitative information.

A great example of a close-ended survey is HubSpot’s annual State of Marketing .

In the State of Marketing, HubSpot asks marketing professionals from around the world a series of multiple-choice questions to gather data on the state of the marketing industry and to identify trends.

The survey covers various topics related to marketing strategies, tactics, tools, and challenges that marketers face. It aims to provide benchmarks to help you make informed decisions about your marketing.

It also helps us understand where our customers’ heads are so we can better evolve our products to meet their needs.

Apple is no stranger to surveys, either.

In 2011, the tech giant launched Apple Customer Pulse , which it described as “an online community of Apple product users who provide input on a variety of subjects and issues concerning Apple.”

"For example, we did a large voluntary survey of email subscribers and top readers a few years back."

While these readers gave us a long list of topics, formats, or content types they wanted to see, they sometimes engaged more with content types they didn’t select or favor as much on the surveys when we ran follow-up ‘in the wild’ tests, like A/B testing.”  

Pepsi saw similar results when it ran its iconic field experiment, “The Pepsi Challenge” for the first time in 1975.

The beverage brand set up tables at malls, beaches, and other public locations and ran a blindfolded taste test. Shoppers were given two cups of soda, one containing Pepsi, the other Coca-Cola (Pepsi’s biggest competitor). They were then asked to taste both and report which they preferred.

People overwhelmingly preferred Pepsi, and the brand has repeated the experiment multiple times over the years to the same results.

What I like: It yields qualitative and quantitative data and can make for engaging marketing content, especially in the digital age.

What I dislike: It can be very time-consuming. And, if you’re not careful, there is a high risk for scientific error.

Best for: Product testing and competitive analysis

Pro tip:  " Don’t make critical business decisions off of just one data set," advises Pamela Bump. "Use the survey, competitive intelligence, external data, or even a focus group to give you one layer of ideas or a short-list for improvements or solutions to test. Then gather your own fresh data to test in an experiment or trial and better refine your data-backed strategy."

Secondary Research

8. public domain or third-party research.

While original data is always a plus, there are plenty of external resources you can access online and even at a library when you’re limited on time or resources.

Some reputable resources you can use include:

• Pew Research Center
• McKinley Global Institute
• Relevant Global or Government Organizations (i.e United Nations or NASA)

It’s also smart to turn to reputable organizations that are specific to your industry or field. For instance, if you’re a gardening or landscaping company, you may want to pull statistics from the Environmental Protection Agency (EPA).

If you’re a digital marketing agency, you could look to Google Research or HubSpot Research . (Hey, I know them!)

What I like: You can save time on gathering data and spend more time on analyzing. You can also rest assured the data is from a source you trust.

What I dislike: You may not find data specific to your needs.

Best for: Companies under a time or resource crunch, adding factual support to content

Pro tip: Fellow HubSpotter Iskiev suggests using third-party data to inspire your original research. “Sometimes, I use public third-party data for ideas and inspiration. Once I have written my survey and gotten all my ideas out, I read similar reports from other sources and usually end up with useful additions for my own research.”

9. Buy Research

If the data you need isn’t available publicly and you can’t do your own market research, you can also buy some. There are many reputable analytics companies that offer subscriptions to access their data. Statista is one of my favorites, but there’s also Euromonitor , Mintel , and BCC Research .

What I like: Same as public domain research

What I dislike: You may not find data specific to your needs. It also adds to your expenses.

Best for: Companies under a time or resource crunch or adding factual support to content

Which marketing research method should you use?

You’re not going to like my answer, but “it depends.” The best marketing research method for you will depend on your objective and data needs, but also your budget and timeline.

My advice? Aim for a mix of quantitative and qualitative data. If you can do your own original research, awesome. But if not, don’t beat yourself up. Lean into free or low-cost tools . You could do primary research for qualitative data, then tap public sources for quantitative data. Or perhaps the reverse is best for you.

Whatever your marketing research method mix, take the time to think it through and ensure you’re left with information that will truly help you achieve your goals.

Don't forget to share this post!

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Title Page Setup

A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.

Student title page

The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.

Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6

Related handouts

• Student Title Page Guide (PDF, 263KB)
• Student Paper Setup Guide (PDF, 3MB)

Student papers do not include a running head unless requested by the instructor or institution.

Follow the guidelines described next to format each element of the student title page.

Paper title	Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms.
Author names	Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name.	Cecily J. Sinclair and Adam Gonzaga
Author affiliation	For a student paper, the affiliation is the institution where the student attends school. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author name(s).	Department of Psychology, University of Georgia
Course number and name	Provide the course number as shown on instructional materials, followed by a colon and the course name. Center the course number and name on the next double-spaced line after the author affiliation.	PSY 201: Introduction to Psychology
Instructor name	Provide the name of the instructor for the course using the format shown on instructional materials. Center the instructor name on the next double-spaced line after the course number and name.	Dr. Rowan J. Estes
Assignment due date	Provide the due date for the assignment. Center the due date on the next double-spaced line after the instructor name. Use the date format commonly used in your country.	October 18, 2020 18 October 2020
	Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header.	1

Professional title page

The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.

Follow the guidelines described next to format each element of the professional title page.

Paper title	Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms.
Author names	Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name.	Francesca Humboldt
	When different authors have different affiliations, use superscript numerals after author names to connect the names to the appropriate affiliation(s). If all authors have the same affiliation, superscript numerals are not used (see Section 2.3 of the for more on how to set up bylines and affiliations).	Tracy Reuter , Arielle Borovsky , and Casey Lew-Williams
Author affiliation	For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center each affiliation on its own line.	Department of Nursing, Morrigan University
	When different authors have different affiliations, use superscript numerals before affiliations to connect the affiliations to the appropriate author(s). Do not use superscript numerals if all authors share the same affiliations (see Section 2.3 of the for more).	Department of Psychology, Princeton University Department of Speech, Language, and Hearing Sciences, Purdue University
Author note	Place the author note in the bottom half of the title page. Center and bold the label “Author Note.” Align the paragraphs of the author note to the left. For further information on the contents of the author note, see Section 2.7 of the .	n/a
	The running head appears in all-capital letters in the page header of all pages, including the title page. Align the running head to the left margin. Do not use the label “Running head:” before the running head.	Prediction errors support children’s word learning
	Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header.	1

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How AI Can Power Brand Management

• Julian De Freitas

Marketers have begun experimenting with AI to improve their brand-management efforts. But unlike other marketing tasks, brand management involves more than just repeatedly executing one specialized function. Long considered the exclusive domain of creative talent, it encompasses multiple activities designed to build the reputation and image of a business—such as crafting and communicating the brand story, ensuring that the product or service and its price reflect the brand’s competitive positioning, and managing customer relationships to forge loyalty to the brand.

A brand is a promise to customers about the quality, style, reliability, and aspiration of a purchase. AI can’t fulfill that promise on its own (at least not anytime soon). But it can shape customers’ impressions of a brand at every interaction. And it can automate expensive creative tasks—including product design. To succeed with it, you must understand how it is perceived by stakeholders and what can be done not only to mitigate their concerns but to make them avid supporters. Using examples from Intuit, Caterpillar, and LOOP, along with in-depth scholarly research, the authors propose a framework for thinking about the key roles that AI plays when it comes to managing brands effectively.

It can automate creative tasks and improve the customer experience.

Idea in Brief

The opportunity.

Brand management, long considered the exclusive domain of creative talent, has become faster and better informed than ever because of AI.

The Challenge

AI has the potential to adversely affect a brand, so successfully implementing it in this context often involves confronting resistance and backlash from both customers and employees.

The Solution

The most successful brand management blends the best of human and machine intelligence to augment rather than replace human creativity. Nike, Intuit, Caterpillar, and others have used AI to the great benefit of their brands.

Few brands are more iconic than Nike. From its swoosh logo to its slogan “Just Do It,” the company has mastered the artistry necessary to build a renowned brand. So when Nike asked Obvious, a trio of Parisian artists who make AI-inspired designs, to develop new iterations of the Air Max sneaker in 2020, it wanted to be sure the designs wouldn’t deviate too dramatically from Nike’s signature style. Obvious trained its generative AI model by feeding it pictures of the Air Max 1, the Air Max 90, and the Air Max 97 and used the model to create a vast array of design ideas. Then, drawing on their own knowledge and perception of broader fashion trends along with Nike’s marketing objectives, the trio iteratively tweaked the model until it produced a design that struck the right balance between novelty and staying on brand. The design incorporated many of the stylistic elements of the classic Air Max but blended them with new colors, shapes, and patterns to achieve a fresh, cool feel. The limited edition shoes sold out in less than 10 days.

• Julian De Freitas is an assistant professor in the marketing unit at Harvard Business School.
• EO Elie Ofek is the Malcolm P. McNair Professor of Marketing at Harvard Business School.

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