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The Craft of Writing a Strong Hypothesis

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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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Hypothesis PPT Presentation | PDF for Free Download

HYPOTHESIS PPT Presentation

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A hypothesis (plural hypotheses) may be a planned clarification for a development. Scientists typically base scientific hypotheses on previous observations that can’t satisfactorily be explained with the accessible scientific theories. Although the words “hypothesis” and “theory” are typically used synonymously, a scientific hypothesis isn’t identical to a theory. An operating hypothesis may be a provisionally accepted hypothesis planned for additional analysis.

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Imagine you have got a test in school tomorrow. You keep out late and see a motion-picture show with friends. you recognize that once you study the night before, you get sensible grades. What does one suppose can happen on tomorrow’s test?

HYPOTHESIS PPT | PDF for Free Download

When you answered this question, you shaped a hypothesis. A hypothesis could be a specific, testable prediction. It describes in concrete terms what you expect can happen during a bound circumstance. Your hypothesis could be, ‘If not studying lowers test performance and that I don’t study, then I’ll get a low grade on the test.’

Purpose of Hypothesis:

A hypothesis is employed in an experiment to outline the link between 2 variables. the aim of a hypothesis is to search out the solution to an issue. A formalized hypothesis can force us to accept what results we must always rummage around for in an experiment. The first variable is termed the independent variable or variable quantity. this is often the part of the experiment which will be modified and tested. The variable quantity happens 1st and may be thought of the explanation for any changes in the outcome. the result is termed the dependent variable. The independent variable in our previous example isn’t learning for a test. The dependent variable that you simply are using to measure outcome is your test score.

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Let’s use the previous example once more, for example, these ideas. The hypothesis is checkable as a result of you may receive a score on your test performance. it’s measurable as a result of you’ll compare check scores received from once you did study and check scores received from once you didn’t study.

A hypothesis should possess the following features:

• It should explain what you expect to happen
• It should be clear
• It should be understandable
• It should be testable
• It should be measurable
• It should contain a dependent variable
• It should contain an independent variable

Developing a Hypothesis:

Another vital side of a hypothesis is that it ought to be supported analysis. bear in mind that the aim of a hypothesis is to search out the solution to a matter. the primary factor you ought to do if you wish to answer a matter is to search out the maximum amount info on the subject as you’ll be able to. Before you return up to a particular hypothesis, pay your time doing an analysis. Then, begin thinking of queries you continue to have. When totally researching your question, you ought to have an informed guess regarding however things work. This guess regarding the solution to your question is wherever your hypothesis comes from.

Let’s imagine that you simply need to grasp why the leaves on the tree in your yard change color in the autumn. First, you’d analysis this development. You observe what you see happen and read concerning the topic. You discover that the color amendment happens once the temperature cools. What question will this data cause you to ask? You come back up with the subsequent question: ‘Does temperature cause the leaves to vary color on the tree in my front yard?’ Next, you raise yourself if this will be tested. If it is tested, you may write a hypothesis that states what you expect to search out. Your hypothesis can be ‘If lower temperatures cause leaves to vary color and therefore the temperature close a tree is reduced, then the leaves can change color.’

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Scientific Project Flowchart:

The below flowchart explain about the developing of hypothesis in a scientific project:

Fig1: Flowchart of a Scientific Project

In the above diagram, we can see the implementation of hypothesis in a scientific project.

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Scientific Method Lesson for Middle School: Writing a Hypothesis

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Formulation of hypothesis and testing

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How to Write a Hypothesis

Aug 24, 2014

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How to Write a Hypothesis. Steps of the Scientific Method. Problem/Question? Collect Information Create a Hypothesis Perform an Experiment Analyze Data Conclusion. The hypothesis is the most important part of your investigation!. What is a hypothesis again?? It is an educated guess.

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Steps of the Scientific Method Problem/Question? Collect Information Create a Hypothesis Perform an Experiment Analyze Data Conclusion

The hypothesis is the most important part of your investigation! What is a hypothesis again?? It is an educated guess. It is your prediction about the outcome of the experiment. It explains a cause and effect.

A Hypothesis ... is written in the following way: “If the (independent variable) is (describe change), then the (dependent variable) will (describe measure).” IMPORTANT: You must always write a hypothesis as an “IF...THEN” statement. It shows how the independent and dependent variables are related.

Now Let’s Try! Here is a scenario: Mrs. Smith wants to know if the amount of TV her son, Jeff, watches affects the number of times he wakes up at night.

First, let’s figure out what our variables are... Independent variable? (hint- change) TV time (1 hour, 2 hours, 3 hours, 4 hours) Dependent variable? (hint- measure) How many times Jeff wakes up at night.

And our hypothesis is? If the (amount of TV time) is (increased), then the (number of times Jeff wakes up) will (increase). Sound good?

Let’s try another scenario... Ms. Szydelko wants to find out if using fertilizer on her sunflowers will affect how high they grow. What might her hypothesis be?

Ms. Szydelko says... “If the amount of fertilizer used on the sunflowers increases, then the height of the sunflowers will also increase.”

Ready for some practice? #1 – Put everything away. #2 – I will count you off by 4’s. Remember your number! #3 – After you join your group, have one group member come and get a mini white board, marker, and tissue (for your eraser). #4 – YOU HAVE 2 MINUTES TO DO THIS.

INSTRUCTIONS I will put a problem or question up on the board. You will discuss with your group and come up with a HYPOTHESIS. When I say “show me your answer,” one person from the group holds up the white board. You must pass the whiteboard to a different team member for each question/problem.

Problem #1 Billy Bob wants to know if the amount of time 6th graders at IJHS spent playing video games affects their science test scores.

Problem #2 Davey Jones has seen those commercials that say “Axe” body spray attracts girls. He decides to test if the amount of times he sprays Axe body spray on himself affects the number of girls that approach him during one school day.

Problem #3 Brendan Quinn is setting up an experiment to find out if the weight of a hockey puck has an affect on the speed of his slap shot.

Problem #4 Dr. McDermott is really curious to find out if the amount of time students spend each night on homework affects their grades.

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Type I and Type II Errors in Statistics (with PPT)

“An error does not become truth by reason of multiplied propagation, nor does truth become error because nobody sees it…” Mahatma Gandhi

For the better understanding of statistical errors, it is essential to understand the concept of ‘ Level of significance ’, ‘ Null hypothesis and ‘ Alternate hypothesis ’.

What is ‘Level of Significance?

Ø   Definition :  The Level of significance is the probability of rejecting the null hypothesis in a statistical test when it is true. Ø   The ‘Level of Significance’ in statistics is conventionally set to 0.05 to 0.01 . Ø   The level of significance in statistics denotes the confidence level of an investigator to accept or reject a null hypothesis in the statistical testing. Ø   A level of significance 0.05 denotes 95% confidence in the decision whereas; the level of significance 0.01 denotes 99% confidence. Ø   Such a low level of significance is selected to reduce the erroneous rejection of a null hypothesis (H 0 ) after the statistical testing.

What is Null hypothesis?

Ø   Definition :  The Null hypothesis is a statement that one seeks to nullify with evidence to the contrary.

Ø   The ‘Null hypothesis’ is denoted as H 0 .

Ø  Most commonly, the null hypothesis is a statement that the phenomenon being studied produces NO effect or makes NO difference.

Ø   Example: (a study to investigate the effect of urea on the size of leaf in rice plants)

      Null hypothesis : H 0 – Urea does NOT have any effect on the leaf size of rice plants.

Ø   The null hypothesis is always constructed in a negative sense.

Ø   The statistical tests only test the possible acceptance or rejection of the null hypothesis.

What is an Alternate hypothesis?

Ø   Definition : The Alternate hypothesis is a statement created in the negation of the null hypothesis.

Ø   Alternate hypothesis is denoted as H 1 .

Ø   Usually, the alternate hypothesis is a statement that the phenomenon being studied produces some effect or makes some differences.

      Alternate hypothesis: H1 – Urea have some effects on the leaf size of rice plants.

Ø   The alternate hypothesis is always constructed in a positive sense. (negation of the negative null hypothesis)

Ø   If a statistical test rejects the null hypothesis, the investigator has to accept the alternate hypothesis.

What are ‘statistical errors’?

Ø   There are two situations in which the decision made on data in the statistics become wrong.

Ø   They are called as the Errors in Statistics or Statistical Errors .

Ø   There are Two types of statistical errors, they are:

(1).  Type I Error

(2).  Type II Error

What is Type I error?

Ø   If an H 0 is true, it should NOT be rejected by the statistical test.

Ø   Suppose an investigator made a decision to reject a true H0, then he/she has committed an error, called the Type I error .

Ø   Type I error is the wrong rejection of a true null hypothesis.

Ø   The Type I error is also referred to as the ‘ False Positive ’.

Ø   Because the type I error is detecting an effect that is not present.

How to avoid or reduce the type I error?

Ø   The probability of committing type I error is specified by the level of significance .

Ø   If a high level of significance is selected (0.1 or 0.2) in the statistical test, the probability of rejecting a null hypothesis increases.

Ø   This means that, at high significance level, the chance of committing the type I error is high.

Ø   Thus in order to avoid or reduce the type I error, a fairly low level of significance is selected (0.05 or 0.01).

What is Type II error?

Ø   If the H 0 is false, it should be rejected by the test of hypothesis.

Ø   If an investigator selects a significance level (0.005 or 0.001) much lower than the conventional level, then the probability of rejecting a wrong null hypothesis reduces.

Ø   Thus the investigator is said to be committed the type II error .

Ø   Type II error is the wrong acceptance of a false (wrong) null hypothesis.

Ø   The type II error is also referred as ‘ false negative ’.

Ø   Because the type II error is the failure to detect an effect that is actually present.

How to avoid or reduce the type II error?

Ø   If the null hypothesis in hypothesis testing is failed to be rejected when it should have been rejected, the type II error is said to have been committed.

Ø   Lower levels of significance increase the chance of type II error in statistical test.

Ø   Thus in order to avoid the type II error, very low level of significance should not be selected in the statistical test.

Key questions

1.  What is level of significance? 2.  What is null hypothesis? Give an example 3.  What is alternate hypothesis? Give an example 4. What are statistical errors? 5.  What is type I error? 6.  How to reduce the chance of committing type I error? 7.  What is type II error 8.  How to reduce the chance of committing type II error?

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Errors in Statistics PPT (Type I and Type II Errors)

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