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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."

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scientific method

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• University of Nevada, Reno - College of Agriculture, Biotechnology and Natural Resources Extension - The Scientific Method
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scientific method , mathematical and experimental technique employed in the sciences . More specifically, it is the technique used in the construction and testing of a scientific hypothesis .

The process of observing, asking questions, and seeking answers through tests and experiments is not unique to any one field of science. In fact, the scientific method is applied broadly in science, across many different fields. Many empirical sciences, especially the social sciences , use mathematical tools borrowed from probability theory and statistics , together with outgrowths of these, such as decision theory , game theory , utility theory, and operations research . Philosophers of science have addressed general methodological problems, such as the nature of scientific explanation and the justification of induction .

The scientific method is critical to the development of scientific theories , which explain empirical (experiential) laws in a scientifically rational manner. In a typical application of the scientific method, a researcher develops a hypothesis , tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments. The modified hypothesis is then retested, further modified, and tested again, until it becomes consistent with observed phenomena and testing outcomes. In this way, hypotheses serve as tools by which scientists gather data. From that data and the many different scientific investigations undertaken to explore hypotheses, scientists are able to develop broad general explanations, or scientific theories.

See also Mill’s methods ; hypothetico-deductive method .

What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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 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).

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.

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Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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Words have precise meanings in science. For example, "theory," "law," and "hypothesis" don't all mean the same thing. Outside of science, you might say something is "just a theory," meaning it's a supposition that may or may not be true. In science, however, a theory is an explanation that generally is accepted to be true. Here's a closer look at these important, commonly misused terms.

A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true.

Example: If you see no difference in the cleaning ability of various laundry detergents, you might hypothesize that cleaning effectiveness is not affected by which detergent you use. This hypothesis can be disproven if you observe a stain is removed by one detergent and not another. On the other hand, you cannot prove the hypothesis. Even if you never see a difference in the cleanliness of your clothes after trying 1,000 detergents, there might be one more you haven't tried that could be different.

Scientists often construct models to help explain complex concepts. These can be physical models like a model volcano or atom  or conceptual models like predictive weather algorithms. A model doesn't contain all the details of the real deal, but it should include observations known to be valid.

Example: The  Bohr model shows electrons orbiting the atomic nucleus, much the same way as the way planets revolve around the sun. In reality, the movement of electrons is complicated but the model makes it clear that protons and neutrons form a nucleus and electrons tend to move around outside the nucleus.

A scientific theory summarizes a hypothesis or group of hypotheses that have been supported with repeated testing. A theory is valid as long as there is no evidence to dispute it. Therefore, theories can be disproven. Basically, if evidence accumulates to support a hypothesis, then the hypothesis can become accepted as a good explanation of a phenomenon. One definition of a theory is to say that it's an accepted hypothesis.

Example: It is known that on June 30, 1908, in Tunguska, Siberia, there was an explosion equivalent to the detonation of about 15 million tons of TNT. Many hypotheses have been proposed for what caused the explosion. It was theorized that the explosion was caused by a natural extraterrestrial phenomenon , and was not caused by man. Is this theory a fact? No. The event is a recorded fact. Is this theory, generally accepted to be true, based on evidence to-date? Yes. Can this theory be shown to be false and be discarded? Yes.

A scientific law generalizes a body of observations. At the time it's made, no exceptions have been found to a law. Scientific laws explain things but they do not describe them. One way to tell a law and a theory apart is to ask if the description gives you the means to explain "why." The word "law" is used less and less in science, as many laws are only true under limited circumstances.

Example: Consider Newton's Law of Gravity . Newton could use this law to predict the behavior of a dropped object but he couldn't explain why it happened.

As you can see, there is no "proof" or absolute "truth" in science. The closest we get are facts, which are indisputable observations. Note, however, if you define proof as arriving at a logical conclusion, based on the evidence, then there is "proof" in science. Some work under the definition that to prove something implies it can never be wrong, which is different. If you're asked to define the terms hypothesis, theory, and law, keep in mind the definitions of proof and of these words can vary slightly depending on the scientific discipline. What's important is to realize they don't all mean the same thing and cannot be used interchangeably.

• Theory Definition in Science
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• Hypothesis Definition (Science)
• The Basics of Physics in Scientific Study
• What Is the Difference Between Hard and Soft Science?
• Deductive Versus Inductive Reasoning
• Hypothesis, Model, Theory, and Law
• Science Projects for Every Subject
• Is Anthropology a Science?
• A Brief History of Atomic Theory
• Usage and Examples of a Rebuttal
• Fallacies of Relevance: Appeal to Authority
• Social Constructionism Definition and Examples
• Scientific Hypothesis Examples
• What Is Belief Perseverance? Definition and Examples

What Are The Steps Of The Scientific Method?

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Science is not just knowledge. It is also a method for obtaining knowledge. Scientific understanding is organized into theories.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

It involves careful observation, asking questions, formulating hypotheses, experimental testing, and refining hypotheses based on experimental findings.

How it is Used

The scientific method can be applied broadly in science across many different fields, such as chemistry, physics, geology, and psychology. In a typical application of this process, a researcher will develop a hypothesis, test this hypothesis, and then modify the hypothesis based on the outcomes of the experiment.

The process is then repeated with the modified hypothesis until the results align with the observed phenomena. Detailed steps of the scientific method are described below.

Keep in mind that the scientific method does not have to follow this fixed sequence of steps; rather, these steps represent a set of general principles or guidelines.

7 Steps of the Scientific Method

Psychology uses an empirical approach.

Empiricism (founded by John Locke) states that the only source of knowledge comes through our senses – e.g., sight, hearing, touch, etc.

Empirical evidence does not rely on argument or belief. Thus, empiricism is the view that all knowledge is based on or may come from direct observation and experience.

The empiricist approach of gaining knowledge through experience quickly became the scientific approach and greatly influenced the development of physics and chemistry in the 17th and 18th centuries.

Step 1: Make an Observation (Theory Construction)

Every researcher starts at the very beginning. Before diving in and exploring something, one must first determine what they will study – it seems simple enough!

By making observations, researchers can establish an area of interest. Once this topic of study has been chosen, a researcher should review existing literature to gain insight into what has already been tested and determine what questions remain unanswered.

This assessment will provide helpful information about what has already been comprehended about the specific topic and what questions remain, and if one can go and answer them.

Specifically, a literature review might implicate examining a substantial amount of documented material from academic journals to books dating back decades. The most appropriate information gathered by the researcher will be shown in the introduction section or abstract of the published study results.

The background material and knowledge will help the researcher with the first significant step in conducting a psychology study, which is formulating a research question.

This is the inductive phase of the scientific process. Observations yield information that is used to formulate theories as explanations. A theory is a well-developed set of ideas that propose an explanation for observed phenomena.

Inductive reasoning moves from specific premises to a general conclusion. It starts with observations of phenomena in the natural world and derives a general law.

Step 2: Ask a Question

Once a researcher has made observations and conducted background research, the next step is to ask a scientific question. A scientific question must be defined, testable, and measurable.

A useful approach to develop a scientific question is: “What is the effect of…?” or “How does X affect Y?”

To answer an experimental question, a researcher must identify two variables: the independent and dependent variables.

The independent variable is the variable manipulated (the cause), and the dependent variable is the variable being measured (the effect).

An example of a research question could be, “Is handwriting or typing more effective for retaining information?” Answering the research question and proposing a relationship between the two variables is discussed in the next step.

Step 3: Form a Hypothesis (Make Predictions)

A hypothesis is an educated guess about the relationship between two or more variables. A hypothesis is an attempt to answer your research question based on prior observation and background research. Theories tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

For example, a researcher might ask about the connection between sleep and educational performance. Do students who get less sleep perform worse on tests at school?

It is crucial to think about different questions one might have about a particular topic to formulate a reasonable hypothesis. It would help if one also considered how one could investigate the causalities.

It is important that the hypothesis is both testable against reality and falsifiable. This means that it can be tested through an experiment and can be proven wrong.

The falsification principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory to be considered scientific, it must be able to be tested and conceivably proven false.

To test a hypothesis, we first assume that there is no difference between the populations from which the samples were taken. This is known as the null hypothesis and predicts that the independent variable will not influence the dependent variable.

Examples of “if…then…” Hypotheses:

• If one gets less than 6 hours of sleep, then one will do worse on tests than if one obtains more rest.
• If one drinks lots of water before going to bed, one will have to use the bathroom often at night.
• If one practices exercising and lighting weights, then one’s body will begin to build muscle.

The research hypothesis is often called the alternative hypothesis and predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Although one could state and write a scientific hypothesis in many ways, hypotheses are usually built like “if…then…” statements.

Step 4: Run an Experiment (Gather Data)

The next step in the scientific method is to test your hypothesis and collect data. A researcher will design an experiment to test the hypothesis and gather data that will either support or refute the hypothesis.

The exact research methods used to examine a hypothesis depend on what is being studied. A psychologist might utilize two primary forms of research, experimental research, and descriptive research.

The scientific method is objective in that researchers do not let preconceived ideas or biases influence the collection of data and is systematic in that experiments are conducted in a logical way.

Experimental Research

Experimental research is used to investigate cause-and-effect associations between two or more variables. This type of research systematically controls an independent variable and measures its effect on a specified dependent variable.

Experimental research involves manipulating an independent variable and measuring the effect(s) on the dependent variable. Repeating the experiment multiple times is important to confirm that your results are accurate and consistent.

One of the significant advantages of this method is that it permits researchers to determine if changes in one variable cause shifts in each other.

While experiments in psychology typically have many moving parts (and can be relatively complex), an easy investigation is rather fundamental. Still, it does allow researchers to specify cause-and-effect associations between variables.

Most simple experiments use a control group, which involves those who do not receive the treatment, and an experimental group, which involves those who do receive the treatment.

An example of experimental research would be when a pharmaceutical company wants to test a new drug. They give one group a placebo (control group) and the other the actual pill (experimental group).

Descriptive Research

Descriptive research is generally used when it is challenging or even impossible to control the variables in question. Examples of descriptive analysis include naturalistic observation, case studies , and correlation studies .

One example of descriptive research includes phone surveys that marketers often use. While they typically do not allow researchers to identify cause and effect, correlational studies are quite common in psychology research. They make it possible to spot associations between distinct variables and measure the solidity of those relationships.

Step 5: Analyze the Data and Draw Conclusions

Once a researcher has designed and done the investigation and collected sufficient data, it is time to inspect this gathered information and judge what has been found. Researchers can summarize the data, interpret the results, and draw conclusions based on this evidence using analyses and statistics.

Upon completion of the experiment, you can collect your measurements and analyze the data using statistics. Based on the outcomes, you will either reject or confirm your hypothesis.

Analyze the Data

So, how does a researcher determine what the results of their study mean? Statistical analysis can either support or refute a researcher’s hypothesis and can also be used to determine if the conclusions are statistically significant.

When outcomes are said to be “statistically significant,” it is improbable that these results are due to luck or chance. Based on these observations, investigators must then determine what the results mean.

An experiment will support a hypothesis in some circumstances, but sometimes it fails to be truthful in other cases.

What occurs if the developments of a psychology investigation do not endorse the researcher’s hypothesis? It does mean that the study was worthless. Simply because the findings fail to defend the researcher’s hypothesis does not mean that the examination is not helpful or instructive.

This kind of research plays a vital role in supporting scientists in developing unexplored questions and hypotheses to investigate in the future. After decisions have been made, the next step is to communicate the results with the rest of the scientific community.

This is an integral part of the process because it contributes to the general knowledge base and can assist other scientists in finding new research routes to explore.

If the hypothesis is not supported, a researcher should acknowledge the experiment’s results, formulate a new hypothesis, and develop a new experiment.

We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist that could refute a theory.

Draw Conclusions and Interpret the Data

When the empirical observations disagree with the hypothesis, a number of possibilities must be considered. It might be that the theory is incorrect, in which case it needs altering, so it fully explains the data.

Alternatively, it might be that the hypothesis was poorly derived from the original theory, in which case the scientists were expecting the wrong thing to happen.

It might also be that the research was poorly conducted, or used an inappropriate method, or there were factors in play that the researchers did not consider. This will begin the process of the scientific method again.

If the hypothesis is supported, the researcher can find more evidence to support their hypothesis or look for counter-evidence to strengthen their hypothesis further.

In either scenario, the researcher should share their results with the greater scientific community.

Step 6: Share Your Results

One of the final stages of the research cycle involves the publication of the research. Once the report is written, the researcher(s) may submit the work for publication in an appropriate journal.

Usually, this is done by writing up a study description and publishing the article in a professional or academic journal. The studies and conclusions of psychological work can be seen in peer-reviewed journals such as  Developmental Psychology , Psychological Bulletin, the  Journal of Social Psychology, and numerous others.

Scientists should report their findings by writing up a description of their study and any subsequent findings. This enables other researchers to build upon the present research or replicate the results.

As outlined by the American Psychological Association (APA), there is a typical structure of a journal article that follows a specified format. In these articles, researchers:

• Supply a brief narrative and background on previous research
• Give their hypothesis
• Specify who participated in the study and how they were chosen
• Provide operational definitions for each variable
• Explain the measures and methods used to collect data
• Describe how the data collected was interpreted
• Discuss what the outcomes mean

A detailed record of psychological studies and all scientific studies is vital to clearly explain the steps and procedures used throughout the study. So that other researchers can try this experiment too and replicate the results.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound. Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

This last step is important because all results, whether they supported or did not support the hypothesis, can contribute to the scientific community. Publication of empirical observations leads to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound.

Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

By replicating studies, psychologists can reduce errors, validate theories, and gain a stronger understanding of a particular topic.

Step 7: Repeat the Scientific Method (Iteration)

Now, if one’s hypothesis turns out to be accurate, find more evidence or find counter-evidence. If one’s hypothesis is false, create a new hypothesis or try again.

One may wish to revise their first hypothesis to make a more niche experiment to design or a different specific question to test.

The amazingness of the scientific method is that it is a comprehensive and straightforward process that scientists, and everyone, can utilize over and over again.

So, draw conclusions and repeat because the scientific method is never-ending, and no result is ever considered perfect.

The scientific method is a process of:

• Making an observation.
• Forming a hypothesis.
• Making a prediction.
• Experimenting to test the hypothesis.

The procedure of repeating the scientific method is crucial to science and all fields of human knowledge.

Further Information

• Karl Popper – Falsification
• Thomas – Kuhn Paradigm Shift
• Positivism in Sociology: Definition, Theory & Examples
• Is Psychology a Science?
• Psychology as a Science (PDF)

List the 6 steps of the scientific methods in order

• Make an observation (theory construction)
• Ask a question. A scientific question must be defined, testable, and measurable.
• Form a hypothesis (make predictions)
• Run an experiment to test the hypothesis (gather data)
• Analyze the data and draw conclusions
• Share your results so that other researchers can make new hypotheses

What is the first step of the scientific method?

The first step of the scientific method is making an observation. This involves noticing and describing a phenomenon or group of phenomena that one finds interesting and wishes to explain.

Observations can occur in a natural setting or within the confines of a laboratory. The key point is that the observation provides the initial question or problem that the rest of the scientific method seeks to answer or solve.

What is the scientific method?

The scientific method is a step-by-step process that investigators can follow to determine if there is a causal connection between two or more variables.

Psychologists and other scientists regularly suggest motivations for human behavior. On a more casual level, people judge other people’s intentions, incentives, and actions daily.

While our standard assessments of human behavior are subjective and anecdotal, researchers use the scientific method to study psychology objectively and systematically.

All utilize a scientific method to study distinct aspects of people’s thinking and behavior. This process allows scientists to analyze and understand various psychological phenomena, but it also provides investigators and others a way to disseminate and debate the results of their studies.

The outcomes of these studies are often noted in popular media, which leads numerous to think about how or why researchers came to the findings they did.

Why Use the Six Steps of the Scientific Method

The goal of scientists is to understand better the world that surrounds us. Scientific research is the most critical tool for navigating and learning about our complex world.

Without it, we would be compelled to rely solely on intuition, other people’s power, and luck. We can eliminate our preconceived concepts and superstitions through methodical scientific research and gain an objective sense of ourselves and our world.

All psychological studies aim to explain, predict, and even control or impact mental behaviors or processes. So, psychologists use and repeat the scientific method (and its six steps) to perform and record essential psychological research.

So, psychologists focus on understanding behavior and the cognitive (mental) and physiological (body) processes underlying behavior.

In the real world, people use to understand the behavior of others, such as intuition and personal experience. The hallmark of scientific research is evidence to support a claim.

Scientific knowledge is empirical, meaning it is grounded in objective, tangible evidence that can be observed repeatedly, regardless of who is watching.

The scientific method is crucial because it minimizes the impact of bias or prejudice on the experimenter. Regardless of how hard one tries, even the best-intentioned scientists can’t escape discrimination. can’t

It stems from personal opinions and cultural beliefs, meaning any mortal filters data based on one’s experience. Sadly, this “filtering” process can cause a scientist to favor one outcome over another.

For an everyday person trying to solve a minor issue at home or work, succumbing to these biases is not such a big deal; in fact, most times, it is important.

But in the scientific community, where results must be inspected and reproduced, bias or discrimination must be avoided.

When to Use the Six Steps of the Scientific Method ?

One can use the scientific method anytime, anywhere! From the smallest conundrum to solving global problems, it is a process that can be applied to any science and any investigation.

Even if you are not considered a “scientist,” you will be surprised to know that people of all disciplines use it for all kinds of dilemmas.

Try to catch yourself next time you come by a question and see how you subconsciously or consciously use the scientific method.

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Scientific Hypotheses: Writing, Promoting, and Predicting Implications

Armen yuri gasparyan.

1 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK.

Lilit Ayvazyan

2 Department of Medical Chemistry, Yerevan State Medical University, Yerevan, Armenia.

Ulzhan Mukanova

3 Department of Surgical Disciplines, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

George D. Kitas

5 Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.

Scientific hypotheses are essential for progress in rapidly developing academic disciplines. Proposing new ideas and hypotheses require thorough analyses of evidence-based data and predictions of the implications. One of the main concerns relates to the ethical implications of the generated hypotheses. The authors may need to outline potential benefits and limitations of their suggestions and target widely visible publication outlets to ignite discussion by experts and start testing the hypotheses. Not many publication outlets are currently welcoming hypotheses and unconventional ideas that may open gates to criticism and conservative remarks. A few scholarly journals guide the authors on how to structure hypotheses. Reflecting on general and specific issues around the subject matter is often recommended for drafting a well-structured hypothesis article. An analysis of influential hypotheses, presented in this article, particularly Strachan's hygiene hypothesis with global implications in the field of immunology and allergy, points to the need for properly interpreting and testing new suggestions. Envisaging the ethical implications of the hypotheses should be considered both by authors and journal editors during the writing and publishing process.

INTRODUCTION

We live in times of digitization that radically changes scientific research, reporting, and publishing strategies. Researchers all over the world are overwhelmed with processing large volumes of information and searching through numerous online platforms, all of which make the whole process of scholarly analysis and synthesis complex and sophisticated.

Current research activities are diversifying to combine scientific observations with analysis of facts recorded by scholars from various professional backgrounds. 1 Citation analyses and networking on social media are also becoming essential for shaping research and publishing strategies globally. 2 Learning specifics of increasingly interdisciplinary research studies and acquiring information facilitation skills aid researchers in formulating innovative ideas and predicting developments in interrelated scientific fields.

Arguably, researchers are currently offered more opportunities than in the past for generating new ideas by performing their routine laboratory activities, observing individual cases and unusual developments, and critically analyzing published scientific facts. What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way. 3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant online databases and promotion platforms.

Although hypotheses are crucially important for the scientific progress, only few highly skilled researchers formulate and eventually publish their innovative ideas per se . Understandably, in an increasingly competitive research environment, most authors would prefer to prioritize their ideas by discussing and conducting tests in their own laboratories or clinical departments, and publishing research reports afterwards. However, there are instances when simple observations and research studies in a single center are not capable of explaining and testing new groundbreaking ideas. Formulating hypothesis articles first and calling for multicenter and interdisciplinary research can be a solution in such instances, potentially launching influential scientific directions, if not academic disciplines.

The aim of this article is to overview the importance and implications of infrequently published scientific hypotheses that may open new avenues of thinking and research.

Despite the seemingly established views on innovative ideas and hypotheses as essential research tools, no structured definition exists to tag the term and systematically track related articles. In 1973, the Medical Subject Heading (MeSH) of the U.S. National Library of Medicine introduced “Research Design” as a structured keyword that referred to the importance of collecting data and properly testing hypotheses, and indirectly linked the term to ethics, methods and standards, among many other subheadings.

One of the experts in the field defines “hypothesis” as a well-argued analysis of available evidence to provide a realistic (scientific) explanation of existing facts, fill gaps in public understanding of sophisticated processes, and propose a new theory or a test. 4 A hypothesis can be proven wrong partially or entirely. However, even such an erroneous hypothesis may influence progress in science by initiating professional debates that help generate more realistic ideas. The main ethical requirement for hypothesis authors is to be honest about the limitations of their suggestions. 5

EXAMPLES OF INFLUENTIAL SCIENTIFIC HYPOTHESES

Daily routine in a research laboratory may lead to groundbreaking discoveries provided the daily accounts are comprehensively analyzed and reproduced by peers. The discovery of penicillin by Sir Alexander Fleming (1928) can be viewed as a prime example of such discoveries that introduced therapies to treat staphylococcal and streptococcal infections and modulate blood coagulation. 6 , 7 Penicillin got worldwide recognition due to the inventor's seminal works published by highly prestigious and widely visible British journals, effective ‘real-world’ antibiotic therapy of pneumonia and wounds during World War II, and euphoric media coverage. 8 In 1945, Fleming, Florey and Chain got a much deserved Nobel Prize in Physiology or Medicine for the discovery that led to the mass production of the wonder drug in the U.S. and ‘real-world practice’ that tested the use of penicillin. What remained globally unnoticed is that Zinaida Yermolyeva, the outstanding Soviet microbiologist, created the Soviet penicillin, which turned out to be more effective than the Anglo-American penicillin and entered mass production in 1943; that year marked the turning of the tide of the Great Patriotic War. 9 One of the reasons of the widely unnoticed discovery of Zinaida Yermolyeva is that her works were published exclusively by local Russian (Soviet) journals.

The past decades have been marked by an unprecedented growth of multicenter and global research studies involving hundreds and thousands of human subjects. This trend is shaped by an increasing number of reports on clinical trials and large cohort studies that create a strong evidence base for practice recommendations. Mega-studies may help generate and test large-scale hypotheses aiming to solve health issues globally. Properly designed epidemiological studies, for example, may introduce clarity to the hygiene hypothesis that was originally proposed by David Strachan in 1989. 10 David Strachan studied the epidemiology of hay fever in a cohort of 17,414 British children and concluded that declining family size and improved personal hygiene had reduced the chances of cross infections in families, resulting in epidemics of atopic disease in post-industrial Britain. Over the past four decades, several related hypotheses have been proposed to expand the potential role of symbiotic microorganisms and parasites in the development of human physiological immune responses early in life and protection from allergic and autoimmune diseases later on. 11 , 12 Given the popularity and the scientific importance of the hygiene hypothesis, it was introduced as a MeSH term in 2012. 13

Hypotheses can be proposed based on an analysis of recorded historic events that resulted in mass migrations and spreading of certain genetic diseases. As a prime example, familial Mediterranean fever (FMF), the prototype periodic fever syndrome, is believed to spread from Mesopotamia to the Mediterranean region and all over Europe due to migrations and religious prosecutions millennia ago. 14 Genetic mutations spearing mild clinical forms of FMF are hypothesized to emerge and persist in the Mediterranean region as protective factors against more serious infectious diseases, particularly tuberculosis, historically common in that part of the world. 15 The speculations over the advantages of carrying the MEditerranean FeVer (MEFV) gene are further strengthened by recorded low mortality rates from tuberculosis among FMF patients of different nationalities living in Tunisia in the first half of the 20th century. 16

Diagnostic hypotheses shedding light on peculiarities of diseases throughout the history of mankind can be formulated using artefacts, particularly historic paintings. 17 Such paintings may reveal joint deformities and disfigurements due to rheumatic diseases in individual subjects. A series of paintings with similar signs of pathological conditions interpreted in a historic context may uncover mysteries of epidemics of certain diseases, which is the case with Ruben's paintings depicting signs of rheumatic hands and making some doctors to believe that rheumatoid arthritis was common in Europe in the 16th and 17th century. 18

WRITING SCIENTIFIC HYPOTHESES

There are author instructions of a few journals that specifically guide how to structure, format, and make submissions categorized as hypotheses attractive. One of the examples is presented by Med Hypotheses , the flagship journal in its field with more than four decades of publishing and influencing hypothesis authors globally. However, such guidance is not based on widely discussed, implemented, and approved reporting standards, which are becoming mandatory for all scholarly journals.

Generating new ideas and scientific hypotheses is a sophisticated task since not all researchers and authors are skilled to plan, conduct, and interpret various research studies. Some experience with formulating focused research questions and strong working hypotheses of original research studies is definitely helpful for advancing critical appraisal skills. However, aspiring authors of scientific hypotheses may need something different, which is more related to discerning scientific facts, pooling homogenous data from primary research works, and synthesizing new information in a systematic way by analyzing similar sets of articles. To some extent, this activity is reminiscent of writing narrative and systematic reviews. As in the case of reviews, scientific hypotheses need to be formulated on the basis of comprehensive search strategies to retrieve all available studies on the topics of interest and then synthesize new information selectively referring to the most relevant items. One of the main differences between scientific hypothesis and review articles relates to the volume of supportive literature sources ( Table 1 ). In fact, hypothesis is usually formulated by referring to a few scientific facts or compelling evidence derived from a handful of literature sources. 19 By contrast, reviews require analyses of a large number of published documents retrieved from several well-organized and evidence-based databases in accordance with predefined search strategies. 20 , 21 , 22

The format of hypotheses, especially the implications part, may vary widely across disciplines. Clinicians may limit their suggestions to the clinical manifestations of diseases, outcomes, and management strategies. Basic and laboratory scientists analysing genetic, molecular, and biochemical mechanisms may need to view beyond the frames of their narrow fields and predict social and population-based implications of the proposed ideas. 23

Advanced writing skills are essential for presenting an interesting theoretical article which appeals to the global readership. Merely listing opposing facts and ideas, without proper interpretation and analysis, may distract the experienced readers. The essence of a great hypothesis is a story behind the scientific facts and evidence-based data.

ETHICAL IMPLICATIONS

The authors of hypotheses substantiate their arguments by referring to and discerning rational points from published articles that might be overlooked by others. Their arguments may contradict the established theories and practices, and pose global ethical issues, particularly when more or less efficient medical technologies and public health interventions are devalued. The ethical issues may arise primarily because of the careless references to articles with low priorities, inadequate and apparently unethical methodologies, and concealed reporting of negative results. 24 , 25

Misinterpretation and misunderstanding of the published ideas and scientific hypotheses may complicate the issue further. For example, Alexander Fleming, whose innovative ideas of penicillin use to kill susceptible bacteria saved millions of lives, warned of the consequences of uncontrolled prescription of the drug. The issue of antibiotic resistance had emerged within the first ten years of penicillin use on a global scale due to the overprescription that affected the efficacy of antibiotic therapies, with undesirable consequences for millions. 26

The misunderstanding of the hygiene hypothesis that primarily aimed to shed light on the role of the microbiome in allergic and autoimmune diseases resulted in decline of public confidence in hygiene with dire societal implications, forcing some experts to abandon the original idea. 27 , 28 Although that hypothesis is unrelated to the issue of vaccinations, the public misunderstanding has resulted in decline of vaccinations at a time of upsurge of old and new infections.

A number of ethical issues are posed by the denial of the viral (human immunodeficiency viruses; HIV) hypothesis of acquired Immune deficiency Syndrome (AIDS) by Peter Duesberg, who overviewed the links between illicit recreational drugs and antiretroviral therapies with AIDS and refuted the etiological role of HIV. 29 That controversial hypothesis was rejected by several journals, but was eventually published without external peer review at Med Hypotheses in 2010. The publication itself raised concerns of the unconventional editorial policy of the journal, causing major perturbations and more scrutinized publishing policies by journals processing hypotheses.

WHERE TO PUBLISH HYPOTHESES

Although scientific authors are currently well informed and equipped with search tools to draft evidence-based hypotheses, there are still limited quality publication outlets calling for related articles. The journal editors may be hesitant to publish articles that do not adhere to any research reporting guidelines and open gates for harsh criticism of unconventional and untested ideas. Occasionally, the editors opting for open-access publishing and upgrading their ethics regulations launch a section to selectively publish scientific hypotheses attractive to the experienced readers. 30 However, the absence of approved standards for this article type, particularly no mandate for outlining potential ethical implications, may lead to publication of potentially harmful ideas in an attractive format.

A suggestion of simultaneously publishing multiple or alternative hypotheses to balance the reader views and feedback is a potential solution for the mainstream scholarly journals. 31 However, that option alone is hardly applicable to emerging journals with unconventional quality checks and peer review, accumulating papers with multiple rejections by established journals.

A large group of experts view hypotheses with improbable and controversial ideas publishable after formal editorial (in-house) checks to preserve the authors' genuine ideas and avoid conservative amendments imposed by external peer reviewers. 32 That approach may be acceptable for established publishers with large teams of experienced editors. However, the same approach can lead to dire consequences if employed by nonselective start-up, open-access journals processing all types of articles and primarily accepting those with charged publication fees. 33 In fact, pseudoscientific ideas arguing Newton's and Einstein's seminal works or those denying climate change that are hardly testable have already found their niche in substandard electronic journals with soft or nonexistent peer review. 34

CITATIONS AND SOCIAL MEDIA ATTENTION

The available preliminary evidence points to the attractiveness of hypothesis articles for readers, particularly those from research-intensive countries who actively download related documents. 35 However, citations of such articles are disproportionately low. Only a small proportion of top-downloaded hypotheses (13%) in the highly prestigious Med Hypotheses receive on average 5 citations per article within a two-year window. 36

With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term. 36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances of citing and crediting authors of genuine research ideas.

A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989, 10 is still attracting numerous citations on Scopus, the largest bibliographic database. As of August 28, 2019, the number of the linked citations in the database is 3,201. Of the citing articles, 160 are cited at least 160 times ( h -index of this research topic = 160). The first three citations are recorded in 1992 and followed by a rapid annual increase in citation activity and a peak of 212 in 2015 ( Fig. 1 ). The top 5 sources of the citations are Clin Exp Allergy (n = 136), J Allergy Clin Immunol (n = 119), Allergy (n = 81), Pediatr Allergy Immunol (n = 69), and PLOS One (n = 44). The top 5 citing authors are leading experts in pediatrics and allergology Erika von Mutius (Munich, Germany, number of publications with the index citation = 30), Erika Isolauri (Turku, Finland, n = 27), Patrick G Holt (Subiaco, Australia, n = 25), David P. Strachan (London, UK, n = 23), and Bengt Björksten (Stockholm, Sweden, n = 22). The U.S. is the leading country in terms of citation activity with 809 related documents, followed by the UK (n = 494), Germany (n = 314), Australia (n = 211), and the Netherlands (n = 177). The largest proportion of citing documents are articles (n = 1,726, 54%), followed by reviews (n = 950, 29.7%), and book chapters (n = 213, 6.7%). The main subject areas of the citing items are medicine (n = 2,581, 51.7%), immunology and microbiology (n = 1,179, 23.6%), and biochemistry, genetics and molecular biology (n = 415, 8.3%).

Interestingly, a recent analysis of 111 publications related to Strachan's hygiene hypothesis, stating that the lack of exposure to infections in early life increases the risk of rhinitis, revealed a selection bias of 5,551 citations on Web of Science. 37 The articles supportive of the hypothesis were cited more than nonsupportive ones (odds ratio adjusted for study design, 2.2; 95% confidence interval, 1.6–3.1). A similar conclusion pointing to a citation bias distorting bibliometrics of hypotheses was reached by an earlier analysis of a citation network linked to the idea that β-amyloid, which is involved in the pathogenesis of Alzheimer disease, is produced by skeletal muscle of patients with inclusion body myositis. 38 The results of both studies are in line with the notion that ‘positive’ citations are more frequent in the field of biomedicine than ‘negative’ ones, and that citations to articles with proven hypotheses are too common. 39

Social media channels are playing an increasingly active role in the generation and evaluation of scientific hypotheses. In fact, publicly discussing research questions on platforms of news outlets, such as Reddit, may shape hypotheses on health-related issues of global importance, such as obesity. 40 Analyzing Twitter comments, researchers may reveal both potentially valuable ideas and unfounded claims that surround groundbreaking research ideas. 41 Social media activities, however, are unevenly distributed across different research topics, journals and countries, and these are not always objective professional reflections of the breakthroughs in science. 2 , 42

Scientific hypotheses are essential for progress in science and advances in healthcare. Innovative ideas should be based on a critical overview of related scientific facts and evidence-based data, often overlooked by others. To generate realistic hypothetical theories, the authors should comprehensively analyze the literature and suggest relevant and ethically sound design for future studies. They should also consider their hypotheses in the context of research and publication ethics norms acceptable for their target journals. The journal editors aiming to diversify their portfolio by maintaining and introducing hypotheses section are in a position to upgrade guidelines for related articles by pointing to general and specific analyses of the subject, preferred study designs to test hypotheses, and ethical implications. The latter is closely related to specifics of hypotheses. For example, editorial recommendations to outline benefits and risks of a new laboratory test or therapy may result in a more balanced article and minimize associated risks afterwards.

Not all scientific hypotheses have immediate positive effects. Some, if not most, are never tested in properly designed research studies and never cited in credible and indexed publication outlets. Hypotheses in specialized scientific fields, particularly those hardly understandable for nonexperts, lose their attractiveness for increasingly interdisciplinary audience. The authors' honest analysis of the benefits and limitations of their hypotheses and concerted efforts of all stakeholders in science communication to initiate public discussion on widely visible platforms and social media may reveal rational points and caveats of the new ideas.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Gasparyan AY, Yessirkepov M, Kitas GD.
• Methodology: Gasparyan AY, Mukanova U, Ayvazyan L.
• Writing - original draft: Gasparyan AY, Ayvazyan L, Yessirkepov M.
• Writing - review & editing: Gasparyan AY, Yessirkepov M, Mukanova U, Kitas GD.

1.2 The Scientific Methods

Section learning objectives.

By the end of this section, you will be able to do the following:

• Explain how the methods of science are used to make scientific discoveries
• Define a scientific model and describe examples of physical and mathematical models used in physics
• Compare and contrast hypothesis, theory, and law

Teacher Support

The learning objectives in this section will help your students master the following standards:

• (A) know the definition of science and understand that it has limitations, as specified in subsection (b)(2) of this section;
• (B) know that scientific hypotheses are tentative and testable statements that must be capable of being supported or not supported by observational evidence. Hypotheses of durable explanatory power which have been tested over a wide variety of conditions are incorporated into theories;
• (C) know that scientific theories are based on natural and physical phenomena and are capable of being tested by multiple independent researchers. Unlike hypotheses, scientific theories are well-established and highly-reliable explanations, but may be subject to change as new areas of science and new technologies are developed;
• (D) distinguish between scientific hypotheses and scientific theories.

Section Key Terms

[OL] Pre-assessment for this section could involve students sharing or writing down an anecdote about when they used the methods of science. Then, students could label their thought processes in their anecdote with the appropriate scientific methods. The class could also discuss their definitions of theory and law, both outside and within the context of science.

[OL] It should be noted and possibly mentioned that a scientist , as mentioned in this section, does not necessarily mean a trained scientist. It could be anyone using methods of science.

Scientific Methods

Scientists often plan and carry out investigations to answer questions about the universe around us. These investigations may lead to natural laws. Such laws are intrinsic to the universe, meaning that humans did not create them and cannot change them. We can only discover and understand them. Their discovery is a very human endeavor, with all the elements of mystery, imagination, struggle, triumph, and disappointment inherent in any creative effort. The cornerstone of discovering natural laws is observation. Science must describe the universe as it is, not as we imagine or wish it to be.

We all are curious to some extent. We look around, make generalizations, and try to understand what we see. For example, we look up and wonder whether one type of cloud signals an oncoming storm. As we become serious about exploring nature, we become more organized and formal in collecting and analyzing data. We attempt greater precision, perform controlled experiments (if we can), and write down ideas about how data may be organized. We then formulate models, theories, and laws based on the data we have collected, and communicate those results with others. This, in a nutshell, describes the scientific method that scientists employ to decide scientific issues on the basis of evidence from observation and experiment.

An investigation often begins with a scientist making an observation . The scientist observes a pattern or trend within the natural world. Observation may generate questions that the scientist wishes to answer. Next, the scientist may perform some research about the topic and devise a hypothesis . A hypothesis is a testable statement that describes how something in the natural world works. In essence, a hypothesis is an educated guess that explains something about an observation.

[OL] An educated guess is used throughout this section in describing a hypothesis to combat the tendency to think of a theory as an educated guess.

Scientists may test the hypothesis by performing an experiment . During an experiment, the scientist collects data that will help them learn about the phenomenon they are studying. Then the scientists analyze the results of the experiment (that is, the data), often using statistical, mathematical, and/or graphical methods. From the data analysis, they draw conclusions. They may conclude that their experiment either supports or rejects their hypothesis. If the hypothesis is supported, the scientist usually goes on to test another hypothesis related to the first. If their hypothesis is rejected, they will often then test a new and different hypothesis in their effort to learn more about whatever they are studying.

Scientific processes can be applied to many situations. Let’s say that you try to turn on your car, but it will not start. You have just made an observation! You ask yourself, "Why won’t my car start?" You can now use scientific processes to answer this question. First, you generate a hypothesis such as, "The car won’t start because it has no gasoline in the gas tank." To test this hypothesis, you put gasoline in the car and try to start it again. If the car starts, then your hypothesis is supported by the experiment. If the car does not start, then your hypothesis is rejected. You will then need to think up a new hypothesis to test such as, "My car won’t start because the fuel pump is broken." Hopefully, your investigations lead you to discover why the car won’t start and enable you to fix it.

A model is a representation of something that is often too difficult (or impossible) to study directly. Models can take the form of physical models, equations, computer programs, or simulations—computer graphics/animations. Models are tools that are especially useful in modern physics because they let us visualize phenomena that we normally cannot observe with our senses, such as very small objects or objects that move at high speeds. For example, we can understand the structure of an atom using models, without seeing an atom with our own eyes. Although images of single atoms are now possible, these images are extremely difficult to achieve and are only possible due to the success of our models. The existence of these images is a consequence rather than a source of our understanding of atoms. Models are always approximate, so they are simpler to consider than the real situation; the more complete a model is, the more complicated it must be. Models put the intangible or the extremely complex into human terms that we can visualize, discuss, and hypothesize about.

Scientific models are constructed based on the results of previous experiments. Even still, models often only describe a phenomenon partially or in a few limited situations. Some phenomena are so complex that they may be impossible to model them in their entirety, even using computers. An example is the electron cloud model of the atom in which electrons are moving around the atom’s center in distinct clouds ( Figure 1.12 ), that represent the likelihood of finding an electron in different places. This model helps us to visualize the structure of an atom. However, it does not show us exactly where an electron will be within its cloud at any one particular time.

As mentioned previously, physicists use a variety of models including equations, physical models, computer simulations, etc. For example, three-dimensional models are often commonly used in chemistry and physics to model molecules. Properties other than appearance or location are usually modelled using mathematics, where functions are used to show how these properties relate to one another. Processes such as the formation of a star or the planets, can also be modelled using computer simulations. Once a simulation is correctly programmed based on actual experimental data, the simulation can allow us to view processes that happened in the past or happen too quickly or slowly for us to observe directly. In addition, scientists can also run virtual experiments using computer-based models. In a model of planet formation, for example, the scientist could alter the amount or type of rocks present in space and see how it affects planet formation.

Scientists use models and experimental results to construct explanations of observations or design solutions to problems. For example, one way to make a car more fuel efficient is to reduce the friction or drag caused by air flowing around the moving car. This can be done by designing the body shape of the car to be more aerodynamic, such as by using rounded corners instead of sharp ones. Engineers can then construct physical models of the car body, place them in a wind tunnel, and examine the flow of air around the model. This can also be done mathematically in a computer simulation. The air flow pattern can be analyzed for regions smooth air flow and for eddies that indicate drag. The model of the car body may have to be altered slightly to produce the smoothest pattern of air flow (i.e., the least drag). The pattern with the least drag may be the solution to increasing fuel efficiency of the car. This solution might then be incorporated into the car design.

Using Models and the Scientific Processes

Be sure to secure loose items before opening the window or door.

In this activity, you will learn about scientific models by making a model of how air flows through your classroom or a room in your house.

• One room with at least one window or door that can be opened
• Work with a group of four, as directed by your teacher. Close all of the windows and doors in the room you are working in. Your teacher may assign you a specific window or door to study.
• Before opening any windows or doors, draw a to-scale diagram of your room. First, measure the length and width of your room using the tape measure. Then, transform the measurement using a scale that could fit on your paper, such as 5 centimeters = 1 meter.
• Your teacher will assign you a specific window or door to study air flow. On your diagram, add arrows showing your hypothesis (before opening any windows or doors) of how air will flow through the room when your assigned window or door is opened. Use pencil so that you can easily make changes to your diagram.
• On your diagram, mark four locations where you would like to test air flow in your room. To test for airflow, hold a strip of single ply tissue paper between the thumb and index finger. Note the direction that the paper moves when exposed to the airflow. Then, for each location, predict which way the paper will move if your air flow diagram is correct.
• Now, each member of your group will stand in one of the four selected areas. Each member will test the airflow Agree upon an approximate height at which everyone will hold their papers.
• When you teacher tells you to, open your assigned window and/or door. Each person should note the direction that their paper points immediately after the window or door was opened. Record your results on your diagram.
• Did the airflow test data support or refute the hypothetical model of air flow shown in your diagram? Why or why not? Correct your model based on your experimental evidence.
• With your group, discuss how accurate your model is. What limitations did it have? Write down the limitations that your group agreed upon.
• Yes, you could use your model to predict air flow through a new window. The earlier experiment of air flow would help you model the system more accurately.
• Yes, you could use your model to predict air flow through a new window. The earlier experiment of air flow is not useful for modeling the new system.
• No, you cannot model a system to predict the air flow through a new window. The earlier experiment of air flow would help you model the system more accurately.
• No, you cannot model a system to predict the air flow through a new window. The earlier experiment of air flow is not useful for modeling the new system.

This Snap Lab! has students construct a model of how air flows in their classroom. Each group of four students will create a model of air flow in their classroom using a scale drawing of the room. Then, the groups will test the validity of their model by placing weathervanes that they have constructed around the room and opening a window or door. By observing the weather vanes, students will see how air actually flows through the room from a specific window or door. Students will then correct their model based on their experimental evidence. The following material list is given per group:

• One room with at least one window or door that can be opened (An optimal configuration would be one window or door per group.)
• Several pieces of construction paper (at least four per group)
• Strips of single ply tissue paper
• One tape measure (long enough to measure the dimensions of the room)
• Group size can vary depending on the number of windows/doors available and the number of students in the class.
• The room dimensions could be provided by the teacher. Also, students may need a brief introduction in how to make a drawing to scale.
• This is another opportunity to discuss controlled experiments in terms of why the students should hold the strips of tissue paper at the same height and in the same way. One student could also serve as a control and stand far away from the window/door or in another area that will not receive air flow from the window/door.
• You will probably need to coordinate this when multiple windows or doors are used. Only one window or door should be opened at a time for best results. Between openings, allow a short period (5 minutes) when all windows and doors are closed, if possible.

Answers to the Grasp Check will vary, but the air flow in the new window or door should be based on what the students observed in their experiment.

Scientific Laws and Theories

A scientific law is a description of a pattern in nature that is true in all circumstances that have been studied. That is, physical laws are meant to be universal , meaning that they apply throughout the known universe. Laws are often also concise, whereas theories are more complicated. A law can be expressed in the form of a single sentence or mathematical equation. For example, Newton’s second law of motion , which relates the motion of an object to the force applied ( F ), the mass of the object ( m ), and the object’s acceleration ( a ), is simply stated using the equation

Scientific ideas and explanations that are true in many, but not all situations in the universe are usually called principles . An example is Pascal’s principle , which explains properties of liquids, but not solids or gases. However, the distinction between laws and principles is sometimes not carefully made in science.

A theory is an explanation for patterns in nature that is supported by much scientific evidence and verified multiple times by multiple researchers. While many people confuse theories with educated guesses or hypotheses, theories have withstood more rigorous testing and verification than hypotheses.

[OL] Explain to students that in informal, everyday English the word theory can be used to describe an idea that is possibly true but that has not been proven to be true. This use of the word theory often leads people to think that scientific theories are nothing more than educated guesses. This is not just a misconception among students, but among the general public as well.

As a closing idea about scientific processes, we want to point out that scientific laws and theories, even those that have been supported by experiments for centuries, can still be changed by new discoveries. This is especially true when new technologies emerge that allow us to observe things that were formerly unobservable. Imagine how viewing previously invisible objects with a microscope or viewing Earth for the first time from space may have instantly changed our scientific theories and laws! What discoveries still await us in the future? The constant retesting and perfecting of our scientific laws and theories allows our knowledge of nature to progress. For this reason, many scientists are reluctant to say that their studies prove anything. By saying support instead of prove , it keeps the door open for future discoveries, even if they won’t occur for centuries or even millennia.

[OL] With regard to scientists avoiding using the word prove , the general public knows that science has proven certain things such as that the heart pumps blood and the Earth is round. However, scientists should shy away from using prove because it is impossible to test every single instance and every set of conditions in a system to absolutely prove anything. Using support or similar terminology leaves the door open for further discovery.

Check Your Understanding

• Models are simpler to analyze.
• Models give more accurate results.
• Models provide more reliable predictions.
• Models do not require any computer calculations.
• They are the same.
• A hypothesis has been thoroughly tested and found to be true.
• A hypothesis is a tentative assumption based on what is already known.
• A hypothesis is a broad explanation firmly supported by evidence.
• A scientific model is a representation of something that can be easily studied directly. It is useful for studying things that can be easily analyzed by humans.
• A scientific model is a representation of something that is often too difficult to study directly. It is useful for studying a complex system or systems that humans cannot observe directly.
• A scientific model is a representation of scientific equipment. It is useful for studying working principles of scientific equipment.
• A scientific model is a representation of a laboratory where experiments are performed. It is useful for studying requirements needed inside the laboratory.
• The hypothesis must be validated by scientific experiments.
• The hypothesis must not include any physical quantity.
• The hypothesis must be a short and concise statement.
• The hypothesis must apply to all the situations in the universe.
• A scientific theory is an explanation of natural phenomena that is supported by evidence.
• A scientific theory is an explanation of natural phenomena without the support of evidence.
• A scientific theory is an educated guess about the natural phenomena occurring in nature.
• A scientific theory is an uneducated guess about natural phenomena occurring in nature.
• A hypothesis is an explanation of the natural world with experimental support, while a scientific theory is an educated guess about a natural phenomenon.
• A hypothesis is an educated guess about natural phenomenon, while a scientific theory is an explanation of natural world with experimental support.
• A hypothesis is experimental evidence of a natural phenomenon, while a scientific theory is an explanation of the natural world with experimental support.
• A hypothesis is an explanation of the natural world with experimental support, while a scientific theory is experimental evidence of a natural phenomenon.

Use the Check Your Understanding questions to assess students’ achievement of the section’s learning objectives. If students are struggling with a specific objective, the Check Your Understanding will help identify which objective and direct students to the relevant content.

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What Is a Hypothesis and How Do I Write One?

General Education

Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including: 

• Defining the term “hypothesis” 
• Providing hypothesis examples 
• Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid. 

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method .  Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

• Observation
• Asking questions
• Forming a hypothesis
• Analyze the data
• Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables 

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables:  independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable. 

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets. 

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement! 

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad.  These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.  

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life: 

• If I get enough sleep, I’ll be able to get more work done tomorrow.
• If the bus is on time, I can make it to my friend’s birthday party. 
• If I study every night this week, I’ll get a better grade on my exam. 

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades). 

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say: 

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.” 

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis! 

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship. 

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think. 

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than  twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against  science, as well. Take this hypothesis example: 

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.” 

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible. 

Good hypotheses ensure that you can observe the results. 

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable. 

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability 

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come  and how many leave. You also write down the temperature on the hour. 

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like: 

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment. 

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this: 

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while: 

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again! 

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

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Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

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The Scientific Method Tutorial

The Scientific Method

Steps in the scientific method.

There is a great deal of variation in the specific techniques scientists use explore the natural world. However, the following steps characterize the majority of scientific investigations:

Step 1: Make observations Step 2: Propose a hypothesis to explain observations Step 3: Test the hypothesis with further observations or experiments Step 4: Analyze data Step 5: State conclusions about hypothesis based on data analysis

Each of these steps is explained briefly below, and in more detail later in this section.

Step 1: Make observations

A scientific inquiry typically starts with observations. Often, simple observations will trigger a question in the researcher's mind.

Example: A biologist frequently sees monarch caterpillars feeding on milkweed plants, but rarely sees them feeding on other types of plants. She wonders if it is because the caterpillars prefer milkweed over other food choices.

Step 2: Propose a hypothesis

The researcher develops a hypothesis (singular) or hypotheses (plural) to explain these observations. A hypothesis is a tentative explanation of a phenomenon or observation(s) that can be supported or falsified by further observations or experimentation.

Example: The researcher hypothesizes that monarch caterpillars prefer to feed on milkweed compared to other common plants. (Notice how the hypothesis is a statement, not a question as in step 1.)

Step 3: Test the hypothesis

The researcher makes further observations and/or may design an experiment to test the hypothesis. An experiment is a controlled situation created by a researcher to test the validity of a hypothesis. Whether further observations or an experiment is used to test the hypothesis will depend on the nature of the question and the practicality of manipulating the factors involved.

Example: The researcher sets up an experiment in the lab in which a number of monarch caterpillars are given a choice between milkweed and a number of other common plants to feed on.

Step 4: Analyze data

The researcher summarizes and analyzes the information, or data, generated by these further observations or experiments.

Example: In her experiment, milkweed was chosen by caterpillars 9 times out of 10 over all other plant selections.

Step 5: State conclusions

The researcher interprets the results of experiments or observations and forms conclusions about the meaning of these results. These conclusions are generally expressed as probability statements about their hypothesis.

Example: She concludes that when given a choice, 90 percent of monarch caterpillars prefer to feed on milkweed over other common plants.

Often, the results of one scientific study will raise questions that may be addressed in subsequent research. For example, the above study might lead the researcher to wonder why monarchs seem to prefer to feed on milkweed, and she may plan additional experiments to explore this question. For example, perhaps the milkweed has higher nutritional value than other available plants.

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The Scientific Method Flowchart

The steps in the scientific method are presented visually in the following flow chart. The question raised or the results obtained at each step directly determine how the next step will proceed. Following the flow of the arrows, pass the cursor over each blue box. An explanation and example of each step will appear. As you read the example given at each step, see if you can predict what the next step will be.

Activity: Apply the Scientific Method to Everyday Life Use the steps of the scientific method described above to solve a problem in real life. Suppose you come home one evening and flick the light switch only to find that the light doesn’t turn on. What is your hypothesis? How will you test that hypothesis? Based on the result of this test, what are your conclusions? Follow your instructor's directions for submitting your response.

The above flowchart illustrates the logical sequence of conclusions and decisions in a typical scientific study. There are some important points to note about this process:

1. The steps are clearly linked.

The steps in this process are clearly linked. The hypothesis, formed as a potential explanation for the initial observations, becomes the focus of the study. The hypothesis will determine what further observations are needed or what type of experiment should be done to test its validity. The conclusions of the experiment or further observations will either be in agreement with or will contradict the hypothesis. If the results are in agreement with the hypothesis, this does not prove that the hypothesis is true! In scientific terms, it "lends support" to the hypothesis, which will be tested again and again under a variety of circumstances before researchers accept it as a fairly reliable description of reality.

2. The same steps are not followed in all types of research.

The steps described above present a generalized method followed in a many scientific investigations. These steps are not carved in stone. The question the researcher wishes to answer will influence the steps in the method and how they will be carried out. For example, astronomers do not perform many experiments as defined here. They tend to rely on observations to test theories. Biologists and chemists have the ability to change conditions in a test tube and then observe whether the outcome supports or invalidates their starting hypothesis, while astronomers are not able to change the path of Jupiter around the Sun and observe the outcome!

3. Collected observations may lead to the development of theories.

When a large number of observations and/or experimental results have been compiled, and all are consistent with a generalized description of how some element of nature operates, this description is called a theory. Theories are much broader than hypotheses and are supported by a wide range of evidence. Theories are important scientific tools. They provide a context for interpretation of new observations and also suggest experiments to test their own validity. Theories are discussed in more detail in another section.

The Scientific Method in Detail

In the sections that follow, each step in the scientific method is described in more detail.

Step 1: Observations

Observations in science.

An observation is some thing, event, or phenomenon that is noticed or observed. Observations are listed as the first step in the scientific method because they often provide a starting point, a source of questions a researcher may ask. For example, the observation that leaves change color in the fall may lead a researcher to ask why this is so, and to propose a hypothesis to explain this phenomena. In fact, observations also will provide the key to answering the research question.

In science, observations form the foundation of all hypotheses, experiments, and theories. In an experiment, the researcher carefully plans what observations will be made and how they will be recorded. To be accepted, scientific conclusions and theories must be supported by all available observations. If new observations are made which seem to contradict an established theory, that theory will be re-examined and may be revised to explain the new facts. Observations are the nuts and bolts of science that researchers use to piece together a better understanding of nature.

Observations in science are made in a way that can be precisely communicated to (and verified by) other researchers. In many types of studies (especially in chemistry, physics, and biology), quantitative observations are used. A quantitative observation is one that is expressed and recorded as a quantity, using some standard system of measurement. Quantities such as size, volume, weight, time, distance, or a host of others may be measured in scientific studies.

Some observations that researchers need to make may be difficult or impossible to quantify. Take the example of color. Not all individuals perceive color in exactly the same way. Even apart from limiting conditions such as colorblindness, the way two people see and describe the color of a particular flower, for example, will not be the same. Color, as perceived by the human eye, is an example of a qualitative observation.

Qualitative observations note qualities associated with subjects or samples that are not readily measured. Other examples of qualitative observations might be descriptions of mating behaviors, human facial expressions, or "yes/no" type of data, where some factor is present or absent. Though the qualities of an object may be more difficult to describe or measure than any quantities associated with it, every attempt is made to minimize the effects of the subjective perceptions of the researcher in the process. Some types of studies, such as those in the social and behavioral sciences (which deal with highly variable human subjects), may rely heavily on qualitative observations.

Question: Why are observations important to science?

Limits of Observations

Because all observations rely to some degree on the senses (eyes, ears, or steady hand) of the researcher, complete objectivity is impossible. Our human perceptions are limited by the physical abilities of our sense organs and are interpreted according to our understanding of how the world works, which can be influenced by culture, experience, or education. According to science education specialist, George F. Kneller, "Surprising as it may seem, there is no fact that is not colored by our preconceptions" ("A Method of Enquiry," from Science and Its Ways of Knowing [Upper Saddle River: Prentice-Hall Inc., 1997], 15).

Observations made by a scientist are also limited by the sensitivity of whatever equipment he is using. Research findings will be limited at times by the available technology. For example, Italian physicist and philosopher Galileo Galilei (1564–1642) was reportedly the first person to observe the heavens with a telescope. Imagine how it must have felt to him to see the heavens through this amazing new instrument! It opened a window to the stars and planets and allowed new observations undreamed of before.

In the centuries since Galileo, increasingly more powerful telescopes have been devised that dwarf the power of that first device. In the past decade, we have marveled at images from deep space , courtesy of the Hubble Space Telescope, a large telescope that orbits Earth. Because of its view from outside the distorting effects of the atmosphere, the Hubble can look 50 times farther into space than the best earth-bound telescopes, and resolve details a tenth of the size (Seeds, Michael A., Horizons: Exploring the Universe , 5 th ed. [Belmont: Wadsworth Publishing Company, 1998], 86-87).

Construction is underway on a new radio telescope that scientists say will be able to detect electromagnetic waves from the very edges of the universe! This joint U.S.-Mexican project may allow us to ask questions about the origins of the universe and the beginnings of time that we could never have hoped to answer before. Completion of the new telescope is expected by the end of 2001.

Although the amount of detail observed by Galileo and today's astronomers is vastly different, the stars and their relationships have not changed very much. Yet with each technological advance, the level of detail of observation has been increased, and with it, the power to answer more and more challenging questions with greater precision.

Question: What are some of the differences between a casual observation and a 'scientific observation'?

Step 2: The Hypothesis

A hypothesis is a statement created by the researcher as a potential explanation for an observation or phenomena. The hypothesis converts the researcher's original question into a statement that can be used to make predictions about what should be observed if the hypothesis is true. For example, given the hypothesis, "exposure to ultraviolet (UV) radiation increases the risk of skin cancer," one would predict higher rates of skin cancer among people with greater UV exposure. These predictions could be tested by comparing skin cancer rates among individuals with varying amounts of UV exposure. Note how the hypothesis itself determines what experiments or further observations should be made to test its validity. Results of tests are then compared to predictions from the hypothesis, and conclusions are stated in terms of whether or not the data supports the hypothesis. So the hypothesis serves a guide to the full process of scientific inquiry.

The Qualities of a Good Hypothesis

• A hypothesis must be testable or provide predictions that are testable. It can potentially be shown to be false by further observations or experimentation.
• A hypothesis should be specific. If it is too general it cannot be tested, or tests will have so many variables that the results will be complicated and difficult to interpret. A well-written hypothesis is so specific it actually determines how the experiment should be set up.
• A hypothesis should not include any untested assumptions if they can be avoided. The hypothesis itself may be an assumption that is being tested, but it should be phrased in a way that does not include assumptions that are not tested in the experiment.
• It is okay (and sometimes a good idea) to develop more than one hypothesis to explain a set of observations. Competing hypotheses can often be tested side-by-side in the same experiment.

Question: Why is the hypothesis important to the scientific method?

Step 3: Testing the Hypothesis

A hypothesis may be tested in one of two ways: by making additional observations of a natural situation, or by setting up an experiment. In either case, the hypothesis is used to make predictions, and the observations or experimental data collected are examined to determine if they are consistent or inconsistent with those predictions. Hypothesis testing, especially through experimentation, is at the core of the scientific process. It is how scientists gain a better understanding of how things work.

Testing a Hypothesis by Observation

Some hypotheses may be tested through simple observation. For example, a researcher may formulate the hypothesis that the sun always rises in the east. What might an alternative hypothesis be? If his hypothesis is correct, he would predict that the sun will rise in the east tomorrow. He can easily test such a prediction by rising before dawn and going out to observe the sunrise. If the sun rises in the west, he will have disproved the hypothesis. He will have shown that it does not hold true in every situation. However, if he observes on that morning that the sun does in fact rise in the east, he has not proven the hypothesis. He has made a single observation that is consistent with, or supports, the hypothesis. As a scientist, to confidently state that the sun will always rise in the east, he will want to make many observations, under a variety of circumstances. Note that in this instance no manipulation of circumstance is required to test the hypothesis (i.e., you aren't altering the sun in any way).

Testing a Hypothesis by Experimentation

An experiment is a controlled series of observations designed to test a specific hypothesis. In an experiment, the researcher manipulates factors related to the hypothesis in such a way that the effect of these factors on the observations (data) can be readily measured and compared. Most experiments are an attempt to define a cause-and-effect relationship between two factors or events—to explain why something happens. For example, with the hypothesis "roses planted in sunny areas bloom earlier than those grown in shady areas," the experiment would be testing a cause-and-effect relationship between sunlight and time of blooming.

A major advantage of setting up an experiment versus making observations of what is already available is that it allows the researcher to control all the factors or events related to the hypothesis, so that the true cause of an event can be more easily isolated. In all cases, the hypothesis itself will determine the way the experiment will be set up. For example, suppose my hypothesis is "the weight of an object is proportional to the amount of time it takes to fall a certain distance." How would you test this hypothesis?

The Qualities of a Good Experiment

• The experiment must be conducted on a group of subjects that are narrowly defined and have certain aspects in common. This is the group to which any conclusions must later be confined. (Examples of possible subjects: female cancer patients over age 40, E. coli bacteria, red giant stars, the nicotine molecule and its derivatives.)
• All subjects of the experiment should be (ideally) completely alike in all ways except for the factor or factors that are being tested. Factors that are compared in scientific experiments are called variables. A variable is some aspect of a subject or event that may differ over time or from one group of subjects to another. For example, if a biologist wanted to test the effect of nitrogen on grass growth, he would apply different amounts of nitrogen fertilizer to several plots of grass. The grass in each of the plots should be as alike as possible so that any difference in growth could be attributed to the effect of the nitrogen. For example, all the grass should be of the same species, planted at the same time and at the same density, receive the same amount of water and sunlight, and so on. The variable in this case would be the amount of nitrogen applied to the plants. The researcher would not compare differing amounts of nitrogen across different grass species to determine the effect of nitrogen on grass growth. What is the problem with using different species of plants to compare the effect of nitrogen on plant growth? There are different kinds of variables in an experiment. A factor that the experimenter controls, and changes intentionally to determine if it has an effect, is called an independent variable . A factor that is recorded as data in the experiment, and which is compared across different groups of subjects, is called a dependent variable . In many cases, the value of the dependent variable will be influenced by the value of an independent variable. The goal of the experiment is to determine a cause-and-effect relationship between independent and dependent variables—in this case, an effect of nitrogen on plant growth. In the nitrogen/grass experiment, (1) which factor was the independent variable? (2) Which factor was the dependent variable?
• Nearly all types of experiments require a control group and an experimental group. The control group generally is not changed in any way, but remains in a "natural state," while the experimental group is modified in some way to examine the effect of the variable which of interest to the researcher. The control group provides a standard of comparison for the experimental groups. For example, in new drug trials, some patients are given a placebo while others are given doses of the drug being tested. The placebo serves as a control by showing the effect of no drug treatment on the patients. In research terminology, the experimental groups are often referred to as treatments , since each group is treated differently. In the experimental test of the effect of nitrogen on grass growth, what is the control group? In the example of the nitrogen experiment, what is the purpose of a control group?
• In research studies a great deal of emphasis is placed on repetition. It is essential that an experiment or study include enough subjects or enough observations for the researcher to make valid conclusions. The two main reasons why repetition is important in scientific studies are (1) variation among subjects or samples and (2) measurement error.

Variation among Subjects

There is a great deal of variation in nature. In a group of experimental subjects, much of this variation may have little to do with the variables being studied, but could still affect the outcome of the experiment in unpredicted ways. For example, in an experiment designed to test the effects of alcohol dose levels on reflex time in 18- to 22-year-old males, there would be significant variation among individual responses to various doses of alcohol. Some of this variation might be due to differences in genetic make-up, to varying levels of previous alcohol use, or any number of factors unknown to the researcher.

Because what the researcher wants to discover is average dose level effects for this group, he must run the test on a number of different subjects. Suppose he performed the test on only 10 individuals. Do you think the average response calculated would be the same as the average response of all 18- to 22-year-old males? What if he tests 100 individuals, or 1,000? Do you think the average he comes up with would be the same in each case? Chances are it would not be. So which average would you predict would be most representative of all 18- to 22-year-old males?

A basic rule of statistics is, the more observations you make, the closer the average of those observations will be to the average for the whole population you are interested in. This is because factors that vary among a population tend to occur most commonly in the middle range, and least commonly at the two extremes. Take human height for example. Although you may find a man who is 7 feet tall, or one who is 4 feet tall, most men will fall somewhere between 5 and 6 feet in height. The more men we measure to determine average male height, the less effect those uncommon extreme (tall or short) individuals will tend to impact the average. Thus, one reason why repetition is so important in experiments is that it helps to assure that the conclusions made will be valid not only for the individuals tested, but also for the greater population those individuals represent.

"The use of a sample (or subset) of a population, an event, or some other aspect of nature for an experimental group that is not large enough to be representative of the whole" is called sampling error (Starr, Cecie, Biology: Concepts and Applications , 4 th ed. [Pacific Cove: Brooks/Cole, 2000], glossary). If too few samples or subjects are used in an experiment, the researcher may draw incorrect conclusions about the population those samples or subjects represent.

Use the jellybean activity below to see a simple demonstration of samping error.

Directions: There are 400 jellybeans in the jar. If you could not see the jar and you initially chose 1 green jellybean from the jar, you might assume the jar only contains green jelly beans. The jar actually contains both green and black jellybeans. Use the "pick 1, 5, or 10" buttons to create your samples. For example, use the "pick" buttons now to create samples of 2, 13, and 27 jellybeans. After you take each sample, try to predict the ratio of green to black jellybeans in the jar. How does your prediction of the ratio of green to black jellybeans change as your sample changes?

Measurement Error

The second reason why repetition is necessary in research studies has to do with measurement error. Measurement error may be the fault of the researcher, a slight difference in measuring techniques among one or more technicians, or the result of limitations or glitches in measuring equipment. Even the most careful researcher or the best state-of-the-art equipment will make some mistakes in measuring or recording data. Another way of looking at this is to say that, in any study, some measurements will be more accurate than others will. If the researcher is conscientious and the equipment is good, the majority of measurements will be highly accurate, some will be somewhat inaccurate, and a few may be considerably inaccurate. In this case, the same reasoning used above also applies here: the more measurements taken, the less effect a few inaccurate measurements will have on the overall average.

Step 4: Data Analysis

In any experiment, observations are made, and often, measurements are taken. Measurements and observations recorded in an experiment are referred to as data . The data collected must relate to the hypothesis being tested. Any differences between experimental and control groups must be expressed in some way (often quantitatively) so that the groups may be compared. Graphs and charts are often used to visualize the data and to identify patterns and relationships among the variables.

Statistics is the branch of mathematics that deals with interpretation of data. Data analysis refers to statistical methods of determining whether any differences between the control group and experimental groups are too great to be attributed to chance alone. Although a discussion of statistical methods is beyond the scope of this tutorial, the data analysis step is crucial because it provides a somewhat standardized means for interpreting data. The statistical methods of data analysis used, and the results of those analyses, are always included in the publication of scientific research. This convention limits the subjective aspects of data interpretation and allows scientists to scrutinize the working methods of their peers.

Why is data analysis an important step in the scientific method?

Step 5: Stating Conclusions

The conclusions made in a scientific experiment are particularly important. Often, the conclusion is the only part of a study that gets communicated to the general public. As such, it must be a statement of reality, based upon the results of the experiment. To assure that this is the case, the conclusions made in an experiment must (1) relate back to the hypothesis being tested, (2) be limited to the population under study, and (3) be stated as probabilities.

The hypothesis that is being tested will be compared to the data collected in the experiment. If the experimental results contradict the hypothesis, it is rejected and further testing of that hypothesis under those conditions is not necessary. However, if the hypothesis is not shown to be wrong, that does not conclusively prove that it is right! In scientific terms, the hypothesis is said to be "supported by the data." Further testing will be done to see if the hypothesis is supported under a number of trials and under different conditions.

If the hypothesis holds up to extensive testing then the temptation is to claim that it is correct. However, keep in mind that the number of experiments and observations made will only represent a subset of all the situations in which the hypothesis may potentially be tested. In other words, experimental data will only show part of the picture. There is always the possibility that a further experiment may show the hypothesis to be wrong in some situations. Also, note that the limits of current knowledge and available technologies may prevent a researcher from devising an experiment that would disprove a particular hypothesis.

The researcher must be sure to limit his or her conclusions to apply only to the subjects tested in the study. If a particular species of fish is shown to consume their young 90 percent of the time when raised in captivity, that doesn't necessarily mean that all fish will do so, or that this fish's behavior would be the same in its native habitat.

Finally, the conclusions of the experiment are generally stated as probabilities. A careful scientist would never say, "drug x kills cancer cells;" she would more likely say, "drug x was shown to destroy 85 percent of cancerous skin cells in rats in lab trials." Notice how very different these two statements are. There is a tendency in the media and in the general public to gravitate toward the first statement. This makes a terrific headline and is also easy to interpret; it is absolute. Remember though, in science conclusions must be confined to the population under study; broad generalizations should be avoided. The second statement is sound science. There is data to back it up. Later studies may reveal a more universal effect of the drug on cancerous cells, or they may not. Most researchers would be unwilling to stake their reputations on the first statement.

As a student, you should read and interpret popular press articles about research studies very carefully. From the text, can you determine how the experiment was set up and what variables were measured? Are the observations and data collected appropriate to the hypothesis being tested? Are the conclusions supported by the data? Are the conclusions worded in a scientific context (as probability statements) or are they generalized for dramatic effect? In any researched-based assignment, it is a good idea to refer to the original publication of a study (usually found in professional journals) and to interpret the facts for yourself.

Qualities of a Good Experiment

• narrowly defined subjects
• all subjects treated alike except for the factor or variable being studied
• a control group is used for comparison
• measurements related to the factors being studied are carefully recorded
• enough samples or subjects are used so that conclusions are valid for the population of interest
• conclusions made relate back to the hypothesis, are limited to the population being studied, and are stated in terms of probabilities

What are the five steps of the scientific method in order from first to last? A:observation, problem, hypothesis, project experimentation, conclusion B: research, problem, project experimentation, conclusion, hypothesis C: research, problem, project experimentation, hypothesis, conclusion project experimentation, conclusion,hypothesis , problem, conclusion

Explanation:

It should be A: observation, problem, hypothesis, project experimentation, conclusion

Usually first you want to a make observations, state the problem that you will figure out, make a hypothesis, test your hypothesis, and make a conclusion saying if your hypothesis to the problem was right or wrong.

Five steps  of the scientific method i n order from first to last is observation, problem, hypothesis, project experimentation, conclusion.

Hypothesis can be defined as an assumption which is made for the sake of argument . It is an interpretation of a practical condition for which action needs to be taken.It is defined as a tentative assumption which is made to test logical consequences .It is an antecedent clause of a statement which is conditional.

It is constructed before research. There are six types of hypothesis 1)simple hypothesis 2) complex hypothesis 3) directional hypothesis 4)non-directional hypothesis 5) null hypothesis 6)casual hypothesis

The hypothesis should be clear and precise , it must be specific and way of explanation of hypothesis should be simple.

Learn more about hypothesis ,here:

https://brainly.com/question/29519577

Related Questions

What scientific laws did Newton present in Principia?

The Principia states Newton's laws of motion, forming the foundation of classical mechanics; Newton's law of universal gravitation; and a derivation of Johannes Kepler's laws of planetary motion (which Kepler had first obtained empirically).

Copy the lists of measurements shown below. Pay close attention to the units that follow each number. List 1: 150 mL 11 mL 200 mL List 2: 2 mL 801 mL 27 cm3 List 3: 1 L 999 mL 998 cm3 a. Cross out the smallest volume in each list. b. Circle the largest volume in each list.

a) cross out 11mL from list 1, 2 mL from list 2, and 998 cm3 from list 3.

b) circle 200 mL from list 1, 801 mL from list 2, and 1 L from list 3.

HELP!!!! Please choose the correct label for this diagram A. Fossil Correlation B. Uniformitarianism C. Faunal Succession D. Fossil Communication System - FCS

Uniformitarianism

I think the B is correct answer

Which of these is NOT a form of acceleration *

Answer: changing directions

Explanation: i took the test

Traveling at a constant speed

When it travels at a constant speed, it is neither changing direction nor speeding up or slowing down.

Rank the nonmetals in each set from most reactive (1) to least reactive (3). Neon: Selenium: Fluorine:

the top one is wrong its actually

The results of an experiment must be _______. kept secret accepted right away repeatable unrepeatable

Hope this helped!

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Understanding Science

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• Understanding Science 101
• Misconceptions
• Testing ideas with evidence is at the heart of the process of science.
• Scientific testing involves figuring out what we would  expect  to observe if an idea were correct and comparing that expectation to what we  actually  observe.

Misconception:  Science proves ideas.

Misconception:  Science can only disprove ideas.

Correction:  Science neither proves nor disproves. It accepts or rejects ideas based on supporting and refuting evidence, but may revise those conclusions if warranted by new evidence or perspectives.  Read more about it.

Testing scientific ideas

Testing ideas about childbed fever.

As a simple example of how scientific testing works, consider the case of Ignaz Semmelweis, who worked as a doctor on a maternity ward in the 1800s. In his ward, an unusually high percentage of new mothers died of what was then called childbed fever. Semmelweis considered many possible explanations for this high death rate. Two of the many ideas that he considered were (1) that the fever was caused by mothers giving birth lying on their backs (as opposed to on their sides) and (2) that the fever was caused by doctors’ unclean hands (the doctors often performed autopsies immediately before examining women in labor). He tested these ideas by considering what expectations each idea generated. If it were true that childbed fever were caused by giving birth on one’s back, then changing procedures so that women labored on their sides should lead to lower rates of childbed fever. Semmelweis tried changing the position of labor, but the incidence of fever did not decrease; the actual observations did not match the expected results. If, however, childbed fever were caused by doctors’ unclean hands, having doctors wash their hands thoroughly with a strong disinfecting agent before attending to women in labor should lead to lower rates of childbed fever. When Semmelweis tried this, rates of fever plummeted; the actual observations matched the expected results, supporting the second explanation.

Testing in the tropics

Let’s take a look at another, very different, example of scientific testing: investigating the origins of coral atolls in the tropics. Consider the atoll Eniwetok (Anewetak) in the Marshall Islands — an oceanic ring of exposed coral surrounding a central lagoon. From the 1800s up until today, scientists have been trying to learn what supports atoll structures beneath the water’s surface and exactly how atolls form. Coral only grows near the surface of the ocean where light penetrates, so Eniwetok could have formed in several ways:

Hypothesis 2: The coral that makes up Eniwetok might have grown in a ring atop an underwater mountain already near the surface. The key to this hypothesis is the idea that underwater mountains don’t sink; instead the remains of dead sea animals (shells, etc.) accumulate on underwater mountains, potentially assisted by tectonic uplifting. Eventually, the top of the mountain/debris pile would reach the depth at which coral grow, and the atoll would form.

Which is a better explanation for Eniwetok? Did the atoll grow atop a sinking volcano, forming an underwater coral tower, or was the mountain instead built up until it neared the surface where coral were eventually able to grow? Which of these explanations is best supported by the evidence? We can’t perform an experiment to find out. Instead, we must figure out what expectations each hypothesis generates, and then collect data from the world to see whether our observations are a better match with one of the two ideas.

If Eniwetok grew atop an underwater mountain, then we would expect the atoll to be made up of a relatively thin layer of coral on top of limestone or basalt. But if it grew upwards around a subsiding island, then we would expect the atoll to be made up of many hundreds of feet of coral on top of volcanic rock. When geologists drilled into Eniwetok in 1951 as part of a survey preparing for nuclear weapons tests, the drill bored through more than 4000 feet (1219 meters) of coral before hitting volcanic basalt! The actual observation contradicted the underwater mountain explanation and matched the subsiding island explanation, supporting that idea. Of course, many other lines of evidence also shed light on the origins of coral atolls, but the surprising depth of coral on Eniwetok was particularly convincing to many geologists.

• Take a sidetrip

Visit the NOAA website to see an animation of coral atoll formation according to Hypothesis 1.

• Teaching resources

Scientists test hypotheses and theories. They are both scientific explanations for what we observe in the natural world, but theories deal with a much wider range of phenomena than do hypotheses. To learn more about the differences between hypotheses and theories, jump ahead to  Science at multiple levels .

• Use our  web interactive  to help students document and reflect on the process of science.
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Hypothesis For Kids

Ai generator.

Crafting a hypothesis isn’t just for scientists in white lab coats; even young budding researchers can join in the fun! When kids learn to frame their curious wonders as hypothesis statements, they pave the way for exciting discoveries. Our guide breaks down the world of hypothesis writing into kid-friendly chunks, complete with relatable thesis statement examples and easy-to-follow tips. Dive in to spark a love for inquiry and nurture young scientific minds!

What is an example of a Hypothesis for Kids?

Question: Do plants grow taller when they are watered with coffee instead of water?

Hypothesis: If I water a plant with coffee instead of water, then the plant will not grow as tall because coffee might have substances that aren’t good for plants.

This hypothesis is based on a simple observation or question a child might have, and it predicts a specific outcome (the plant not growing as tall) due to a specific condition (being watered with coffee). It’s presented in simple language suitable for kids.

100 Kids Hypothesis Statement Examples

Size: 170 KB

Children’s innate curiosity lays the foundation for numerous questions about the world around them. Framing these questions as good hypothesis statements can transform them into exciting learning experiments. Presented below are relatable and straightforward examples crafted especially for young minds, offering them a structured way to articulate their wonders and predictions.

• Sunlight & Plant Growth : If a plant gets more sunlight, then it will grow taller.
• Sugary Drinks & Tooth Decay : Drinking sugary drinks daily will lead to faster tooth decay.
• Chocolates & Energy : Eating chocolate will make me feel more energetic.
• Moon Phases & Sleep : I’ll sleep more during a full moon night.
• Homework & Weekend Moods : If I finish my homework on Friday, I’ll be happier over the weekend.
• Pets & Happiness : Owning a pet will make a child happier.
• Rain & Worms : Worms come out more after it rains.
• Shadows & Time of Day : Shadows are longer in the evening than at noon.
• Snow & School Holidays : More snow means there’s a better chance of school being canceled.
• Ice Cream & Brain Freeze : Eating ice cream too fast will give me a brain freeze.
• Video Games & Dreams : Playing video games before bed might make my dreams more vivid.
• Green Vegetables & Strength : Eating more green vegetables will make me stronger.
• Bicycles & Balance : The more I practice, the better I’ll get at riding my bike without training wheels.
• Stars & Wishes : If I wish on the first star I see at night, my wish might come true.
• Cartoons & Laughing : Watching my favorite cartoon will always make me laugh.
• Soda & Bone Health : Drinking soda every day will make my bones weaker.
• Beach Visits & Sunburn : If I don’t wear sunscreen at the beach, I’ll get sunburned.
• Loud Noises & Pet Behavior : My cat hides when she hears loud noises.
• Bedtime & Morning Energy : Going to bed early will make me feel more energetic in the morning.
• Healthy Snacks & Hunger : Eating a healthy snack will keep me full for longer. …
• Toys & Sharing : The more toys I have, the more I want to share with my friends.
• Homemade Cookies & Taste : Homemade cookies always taste better than store-bought ones.
• Books & Imagination : The more books I read, the more adventures I can imagine.
• Jumping & Height : The more I practice, the higher I can jump.
• Singing & Mood : Singing my favorite song always makes me happy.
• Snowmen & Temperature : If the temperature rises, my snowman will melt faster.
• Costumes & Play : Wearing a costume will make playtime more fun.
• Gardening & Patience : Waiting for my plants to grow teaches me patience.
• Night Lights & Sleep : Having a night light makes it easier for me to sleep.
• Handwriting & Practice : The more I practice, the better my handwriting will become.
• Painting & Creativity : Using more colors in my painting lets me express my creativity better.
• Puzzles & Problem Solving : The more puzzles I solve, the better I become at problem-solving.
• Dancing & Coordination : The more I dance, the more coordinated I will become.
• Stargazing & Constellations : If I stargaze every night, I’ll recognize more constellations.
• Bird Watching & Species Knowledge : The more I watch birds, the more species I can identify.
• Cooking & Skill : If I help in the kitchen often, I’ll become a better cook.
• Swimming & Confidence : The more I swim, the more confident I become in the water.
• Trees & Birds’ Nests : The taller the tree, the more likely it is to have birds’ nests.
• Roller Skating & Balance : If I roller skate every weekend, I’ll improve my balance.
• Drawing & Observation : The more I draw, the better I become at observing details.
• Sandcastles & Water : If I use wet sand, I can build a stronger sandcastle.
• Hiking & Endurance : The more I hike, the farther I can walk without getting tired.
• Camping & Outdoor Skills : If I go camping often, I’ll learn more about surviving outdoors.
• Magic Tricks & Practice : The more I practice a magic trick, the better I’ll get at performing it.
• Stickers & Collection : If I collect stickers, my album will become more colorful.
• Board Games & Strategy : The more board games I play, the better strategist I’ll become.
• Pets & Responsibility : The more I take care of my pet, the more responsible I become.
• Music & Concentration : Listening to calm music while studying will help me concentrate better.
• Photographs & Memories : The more photos I take, the more memories I can preserve.
• Rainbows & Rain : If it rains while the sun is out, I might see a rainbow.
• Museums & Knowledge : Every time I visit a museum, I learn something new.
• Fruits & Health : Eating more fruits will keep me healthier.
• Stories & Vocabulary : The more stories I listen to, the more new words I learn.
• Trees & Fresh Air : The more trees there are in a park, the fresher the air will be.
• Diary & Feelings : Writing in my diary helps me understand my feelings better.
• Planets & Telescopes : If I look through a telescope, I’ll see more planets clearly.
• Crafting & Creativity : The more crafts I make, the more creative I become.
• Snowflakes & Patterns : Every snowflake has a unique pattern.
• Jokes & Laughter : The funnier the joke, the louder I’ll laugh.
• Riddles & Thinking : Solving riddles makes me think harder.
• Nature Walks & Observations : The quieter I am on a nature walk, the more animals I’ll spot.
• Building Blocks & Structures : The more blocks I use, the taller my tower will be.
• Kites & Wind : If there’s more wind, my kite will fly higher.
• Popcorn & Movie Nights : Watching a movie with popcorn makes it more enjoyable.
• Stars & Wishes : If I see a shooting star, I should make a wish.
• Diets & Energy : Eating a balanced diet gives me more energy for playtime.
• Clay & Sculptures : The more I play with clay, the better my sculptures will be.
• Insects & Magnifying Glass : Using a magnifying glass will let me see more details of tiny insects.
• Aquarium Visits & Marine Knowledge : Every time I visit the aquarium, I discover a new marine creature.
• Yoga & Flexibility : If I practice yoga daily, I’ll become more flexible.
• Toothpaste & Bubbles : The more toothpaste I use, the more bubbles I’ll get while brushing.
• Journals & Memories : Writing in my journal every day helps me remember special moments.
• Piggy Banks & Savings : The more coins I save, the heavier my piggy bank will get.
• Baking & Measurements : If I measure ingredients accurately, my cake will turn out better.
• Coloring Books & Art Skills : The more I color, the better I get at staying inside the lines.
• Picnics & Outdoor Fun : Having a picnic makes a sunny day even more enjoyable.
• Recycling & Environment : The more I recycle, the cleaner my environment will be.
• Treasure Hunts & Discoveries : Every treasure hunt has a new discovery waiting.
• Milk & Bone Health : Drinking milk daily will make my bones stronger.
• Puppet Shows & Stories : The more puppet shows I watch, the more stories I learn.
• Field Trips & Learning : Every field trip to a new place teaches me something different.
• Chores & Responsibility : The more chores I do, the more responsible I feel.
• Fishing & Patience : Fishing teaches me to be patient while waiting for a catch.
• Fairy Tales & Imagination : Listening to fairy tales expands my imagination.
• Homemade Pizza & Toppings : The more toppings I add, the tastier my homemade pizza will be.
• Gardens & Butterflies : If I plant more flowers, I’ll see more butterflies in my garden.
• Raincoats & Puddles : Wearing a raincoat lets me jump in puddles without getting wet.
• Gymnastics & Balance : The more I practice gymnastics, the better my balance will be.
• Origami & Craft Skills : The more origami I fold, the better my craft skills become.
• Basketball & Shooting Skills : The more I practice, the better I get at shooting baskets.
• Fireflies & Night Beauty : Catching fireflies makes summer nights magical.
• Books & Knowledge : The more books I read, the smarter I become.
• Pillows & Forts : With more pillows, I can build a bigger fort.
• Lemonade & Summers : Drinking lemonade makes hot summer days refreshing.
• Bicycles & Balance : The more I practice, the better I get at riding my bike without training wheels.
• Pencils & Drawings : If I have colored pencils, my drawings will be more colorful.
• Ice Cream & Happiness : Eating ice cream always makes me happy.
• Beach Visits & Shell Collections : Every time I visit the beach, I find new shells for my collection.
• Jump Ropes & Fitness : The more I jump rope, the fitter I become.
• Tea Parties & Imagination : Hosting tea parties lets my imagination run wild.

Simple Hypothesis Statement Examples for Kids

Simple hypothesis are straightforward predictions that can be tested easily. They help children understand the relationship between two variables. Here are some examples tailored just for kids.

• Plants & Sunlight : Plants placed near the window will grow taller than those in the dark.
• Chocolates & Happiness : Eating chocolates can make kids feel happier.
• Rain & Puddles : The more it rains, the bigger the puddles become.
• Homework & Learning : Doing homework helps kids understand lessons better.
• Toys & Sharing : Sharing toys with friends makes playtime more fun.
• Pets & Care : Taking care of a pet fish helps it live longer.
• Storytime & Sleep : Listening to a bedtime story helps kids sleep faster.
• Brushing & Cavity : Brushing teeth daily prevents cavities.
• Games & Skill : Playing a new game every day improves problem-solving skills.
• Baking & Patience : Waiting for cookies to bake teaches patience.

Hypothesis Statement Examples for Kids Psychology

Child psychology hypothesis delves into how kids think, behave, and process emotions. These hypotheses help understand the psychological aspects of children’s behaviors.

• Emotions & Colors : Kids might feel calm when surrounded by blue and energetic with red.
• Friendship & Self-esteem : Making friends can boost a child’s self-confidence.
• Learning Styles & Memory : Some kids remember better by seeing, while others by doing.
• Play & Development : Pretend play is crucial for cognitive development.
• Rewards & Motivation : Giving small rewards can motivate kids to finish tasks.
• Music & Mood : Listening to soft music can calm a child’s anxiety.
• Sibling Bonds & Sharing : Having siblings can influence a child’s willingness to share.
• Feedback & Performance : Positive feedback can improve a kid’s academic performance.
• Outdoor Play & Attention Span : Playing outside can help kids concentrate better in class.
• Dreams & Reality : Kids sometimes can’t differentiate between dreams and reality.

Hypothesis Examples in Kid Friendly Words

Phrasing hypothesis in simple words makes it relatable and easier for kids to grasp. Here are examples with kid-friendly language.

• Socks & Warmth : Wearing socks will keep my toes toasty.
• Jumping & Energy : The more I jump, the more energy I feel.
• Sandcastles & Water : A little water makes my sandcastle stand tall.
• Stickers & Smiles : Getting a sticker makes my day shine brighter.
• Rainbows & Rain : After the rain, I might see a rainbow.
• Slides & Speed : The taller the slide, the faster I go.
• Hugs & Love : Giving hugs makes me and my friends feel loved.
• Stars & Counting : The darker it is, the more stars I can count.
• Paint & Mess : The more paint I use, the messier it gets.
• Bubbles & Wind : If I blow my bubble wand, the wind will carry them high.

Hypothesis Statement Examples for Kids in Research

Even in a research setting, research hypothesis should be age-appropriate for kids. These examples focus on concepts children might encounter in structured studies.

• Reading & Vocabulary : Kids who read daily might have a richer vocabulary.
• Games & Math Skills : Playing number games can improve math skills.
• Experiments & Curiosity : Conducting science experiments can make kids more curious.
• Doodles & Creativity : Drawing daily might enhance a child’s creativity.
• Learning Methods & Retention : Kids who learn with visuals might remember lessons better.
• Discussions & Understanding : Talking about a topic can deepen understanding.
• Observation & Knowledge : Observing nature can increase a kid’s knowledge about the environment.
• Puzzles & Cognitive Skills : Solving puzzles regularly might enhance logical thinking.
• Music & Rhythmic Abilities : Kids who practice music might develop better rhythm skills.
• Teamwork & Social Skills : Group projects can boost a child’s social skills.

Hypothesis Statement Examples for Kids Science Fair

Science fairs are a chance for kids to delve into the world of experiments and observations. Here are hypotheses suitable for these events.

• Magnet & Metals : Certain metals will be attracted to a magnet.
• Plants & Colored Light : Plants might grow differently under blue and red lights.
• Eggs & Vinegar : An egg in vinegar might become bouncy.
• Solar Panels & Sunlight : Solar panels will generate more power on sunny days.
• Volcanoes & Eruptions : Mixing baking soda and vinegar will make a mini eruption.
• Mirrors & Reflection : Shiny surfaces can reflect light better than dull ones.
• Battery & Energy : Fresh batteries will make a toy run faster.
• Density & Floating : Objects with lower density will float in water.
• Shadows & Light Source : Moving the light source will change the shadow’s direction.
• Freezing & States : Water turns solid when kept in the freezer.

Hypothesis Statement Examples for Science Experiments

Experiments let kids test out their predictions in real-time. Here are hypotheses crafted for various scientific tests.

• Salt & Boiling Point : Adding salt will make water boil at a higher temperature.
• Plants & Music : Playing music might affect a plant’s growth rate.
• Rust & Moisture : Metals kept in a moist environment will rust faster.
• Candles & Oxygen : A candle will burn out faster in an enclosed jar.
• Fruits & Browning : Lemon juice can prevent cut fruits from browning.
• Yeast & Sugar : Adding sugar will make yeast activate more vigorously.
• Density & Layers : Different liquids will form layers based on their density.
• Acids & Bases : Red cabbage juice will change color in acids and bases.
• Soil Types & Water : Sandy soil will drain water faster than clay.
• Thermometers & Temperatures : Thermometers will show higher readings in the sun.

Hypothesis Statement Examples for Kids At Home

These hypotheses are crafted for experiments and observations kids can easily make at home, using everyday items.

• Chores & Time : Setting a timer will make me finish my chores faster.
• Pets & Behavior : My cat sleeps more during the day than at night.
• Recycling & Environment : Recycling more can reduce the trash in my home.
• Cooking & Tastes : Adding spices will change the taste of my food.
• Family Time & Bonding : Playing board games strengthens our family bond.
• Cleaning & Organization : Organizing my toys daily will keep my room tidier.
• Watering & Plant Health : Watering my plant regularly will keep its leaves green.
• Decor & Mood : Changing the room decor can influence my mood.
• Journals & Memories : Writing in my journal daily will help me remember fun events.
• Photos & Growth : Taking monthly photos will show how much I’ve grown.

How do you write a hypothesis for kids? – A Step by Step Guide

Step 1: Start with Curiosity Begin with a question that your child is curious about. This could be something simple, like “Why is the sky blue?” or “Do plants need sunlight to grow?”

Step 2: Observe and Research Before formulating the hypothesis, encourage your child to observe the world around them. If possible, read or watch videos about the topic to gather information. The idea is to get a general understanding of the subject.

Step 3: Keep it Simple For kids, it’s essential to keep the hypothesis straightforward and concise. Use language that is easy to understand and relatable to their age.

Step 4: Make a Predictable Statement Help your child frame their hypothesis as an “If… then…” statement. For example, “If I water a plant every day, then it will grow taller.”

Step 5: Ensure Testability Ensure that the hypothesis can be tested using simple experiments or observations. It should be something they can prove or disprove through hands-on activities.

Step 6: Avoid Certainty Teach kids that a hypothesis is not a definitive statement of fact but rather a best guess based on what they know. It’s okay if the hypothesis turns out to be wrong; the learning process is more important.

Step 7: Review and Refine After forming the initial hypothesis, review it with your child. Discuss if it can be made simpler or clearer. Refinement aids in better understanding and testing.

Step 8: Test the Hypothesis This is the fun part! Plan an experiment or set of observations to test the hypothesis. Whether the hypothesis is proven correct or not, the experience provides a learning opportunity.

Tips for Writing Hypothesis for Kids

• Encourage Curiosity : Always encourage your child to ask questions about the world around them. It’s the first step to formulating a hypothesis.
• Use Familiar Language : Use words that the child understands and can relate to. Avoid jargon or technical terms.
• Make it Fun : Turn the process of forming a hypothesis into a game or a storytelling session. This will keep kids engaged.
• Use Visual Aids : Kids often respond well to visuals. Drawing or using props can help in understanding and formulating the hypothesis.
• Stay Open-minded : It’s essential to teach kids that it’s okay if their hypothesis is wrong. The process of discovery and learning is what’s crucial.
• Practice Regularly : The more often kids practice forming hypotheses, the better they get at it. Use everyday situations as opportunities.
• Link to Real-life Scenarios : Relate the hypothesis to real-life situations or personal experiences. For instance, if discussing plants, you can relate it to a plant you have at home.
• Collaborate : Sometimes, two heads are better than one. Encourage group activities where kids can discuss and come up with hypotheses together.
• Encourage Documentation : Keeping a journal or notebook where they document their hypotheses and results can be a great learning tool.
• Celebrate Efforts : Regardless of whether the hypothesis was correct, celebrate the effort and the learning journey. This reinforces the idea that the process is more important than the outcome.

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A molecular hypothesis on malignant transformation of oral lichen planus: a systematic review and meta-analysis of cancer hallmarks expression in this oral potentially malignant disorder.

Simple Summary

1. introduction, 2. materials and methods, 2.1. protocol, 2.2. search strategy, 2.3. eligibility criteria, 2.4. study selection process, 2.5. data extraction, 2.6. appraisal of quality and risk of bias, 2.7. statistical analysis, 3.1. results of the literature search, 3.2. study characteristics, 3.3. qualitative evaluation, 3.4. quantitative evaluation (meta-analysis), 3.4.1. hallmark 1: sustaining proliferative signaling, 3.4.2. hallmark 2: evading growth suppressors, 3.4.3. hallmark 3: resisting cell death, 3.4.4. hallmark 4: enabling replicative immortality, 3.4.5. hallmark 5: inducing angiogenesis, 3.4.6. hallmark 6: activating invasion and metastasis, 3.4.7. hallmark 7: avoiding immune destruction, 3.4.8. hallmark 8: deregulating cellular energetics, 3.4.9. hallmark 9: genome instability and mutation, 3.4.10. hallmark 10: tumor-promoting inflammation, 3.4.11. unspecified, 3.5. analysis of small-study effects, 4. discussion, 5. conclusions, supplementary materials, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Keim-del Pino, C.; Ramos-García, P.; González-Moles, M.Á. A Molecular Hypothesis on Malignant Transformation of Oral Lichen Planus: A Systematic Review and Meta-Analysis of Cancer Hallmarks Expression in This Oral Potentially Malignant Disorder. Cancers 2024 , 16 , 2614. https://doi.org/10.3390/cancers16152614

Keim-del Pino C, Ramos-García P, González-Moles MÁ. A Molecular Hypothesis on Malignant Transformation of Oral Lichen Planus: A Systematic Review and Meta-Analysis of Cancer Hallmarks Expression in This Oral Potentially Malignant Disorder. Cancers . 2024; 16(15):2614. https://doi.org/10.3390/cancers16152614

Keim-del Pino, Carmen, Pablo Ramos-García, and Miguel Ángel González-Moles. 2024. "A Molecular Hypothesis on Malignant Transformation of Oral Lichen Planus: A Systematic Review and Meta-Analysis of Cancer Hallmarks Expression in This Oral Potentially Malignant Disorder" Cancers 16, no. 15: 2614. https://doi.org/10.3390/cancers16152614

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The Puzzle of How Large-Scale Order Emerges in Complex Systems

The original version of this story appeared in Quanta Magazine .

A few centuries ago, the swirling polychromatic chaos of Jupiter’s atmosphere spawned the immense vortex that we call the Great Red Spot.

From the frantic firing of billions of neurons in your brain comes your unique and coherent experience of reading these words.

As pedestrians each try to weave their path on a crowded sidewalk, they begin to follow one another, forming streams that no one ordained or consciously chose.

The world is full of such emergent phenomena: large-scale patterns and organization arising from innumerable interactions between component parts. And yet there is no agreed scientific theory to explain emergence. Loosely, the behavior of a complex system might be considered emergent if it can’t be predicted from the properties of the parts alone. But when will such large-scale structures and patterns arise, and what’s the criterion for when a phenomenon is emergent and when it isn’t? Confusion has reigned. “It’s just a muddle,” said Jim Crutchfield , a physicist at the University of California, Davis.

“Philosophers have long been arguing about emergence, and going round in circles,” said Anil Seth , a neuroscientist at the University of Sussex in England. The problem, according to Seth, is that we haven’t had the right tools—“not only the tools for analysis, but the tools for thinking. Having measures and theories of emergence would not only be something we can throw at data but would also be tools that can help us think about these systems in a richer way.”

Though the problem remains unsolved, over the past few years, a community of physicists, computer scientists, and neuroscientists has been working toward a better understanding. These researchers have developed theoretical tools for identifying when emergence has occurred. And in February, Fernando Rosas , a complex-systems scientist at Sussex, together with Seth and five coauthors, went further, with a framework for understanding how emergence arises.

Fernando Rosas, a complex-systems scientist at the University of Sussex, suggests thinking of emergent phenomena as “software in the natural world.”

A complex system exhibits emergence, according to the new framework, by organizing itself into a hierarchy of levels that each operate independently of the details of the lower levels. The researchers suggest we think about emergence as a kind of “software in the natural world.” Just as the software of your laptop runs without having to keep track of all the microscale information about the electrons in the computer circuitry, so emergent phenomena are governed by macroscale rules that seem self-contained, without heed to what the component parts are doing.

Using a mathematical formalism called computational mechanics, the researchers identified criteria for determining which systems have this kind of hierarchical structure. They tested these criteria on several model systems known to display emergent-type phenomena, including neural networks and Game of Life–style cellular automata. Indeed, the degrees of freedom, or independent variables, that capture the behavior of these systems at microscopic and macroscopic scales have precisely the relationship that the theory predicts.

No new matter or energy appears at the macroscopic level in emergent systems that isn’t there microscopically, of course. Rather, emergent phenomena, from Great Red Spots to conscious thoughts, demand a new language for describing the system. “What these authors have done is to try to formalize that,” said Chris Adami , a complex-systems researcher at Michigan State University. “I fully applaud this idea of making things mathematical.”

A Need for Closure

Rosas came at the topic of emergence from multiple directions. His father was a famous conductor in Chile, where Rosas first studied and played music. “I grew up in concert halls,” he said. Then he switched to philosophy, followed by a degree in pure mathematics, giving him “an overdose of abstractions” that he “cured” with a PhD in electrical engineering.

A few years ago, Rosas started thinking about the vexed question of whether the brain is a computer. Consider what goes on in your laptop. The software generates predictable and repeatable outputs for a given set of inputs. But if you look at the actual physics of the system, the electrons won’t all follow identical trajectories each time. “It’s a mess,” said Rosas. “It’ll never be exactly the same.”

The software seems to be “closed,” in the sense that it doesn’t depend on the detailed physics of the microelectronic hardware. The brain behaves somewhat like this too: There’s a consistency to our behaviors even though the neural activity is never identical in any circumstance.

Rosas and colleagues figured that in fact there are three different types of closure involved in emergent systems. Would the output of your laptop be any more predictable if you invested lots of time and energy in collecting information about all the microstates—electron energies and so forth—in the system? Generally, no. This corresponds to the case of informational closure : As Rosas put it, “All the details below the macro are not helpful for predicting the macro.”

What if you want not just to predict but to control the system—does the lower-level information help there? Again, typically no: Interventions we make at the macro level, such as changing the software code by typing on the keyboard, are not made more reliable by trying to alter individual electron trajectories. If the lower-level information adds no further control of macro outcomes, the macro level is causally closed : It alone is causing its own future.

Jim Crutchfield, a physicist at the University of California, Davis, is shown with an underwater microphone he recently designed for recording humpback whale vocalizations, to which he is applying his pattern-discovery methods in hopes of decoding the causal relationships between vocalizations.

This situation is rather common. Consider, for instance, that we can use macroscopic variables like pressure and viscosity to talk about (and control) fluid flow, and knowing the positions and trajectories of individual molecules doesn’t add useful information for those purposes. And we can describe the market economy by considering companies as single entities, ignoring any details about the individuals that constitute them.

The existence of a useful coarse-grained description doesn’t, however, by itself define an emergent phenomenon, said Seth. “You want to say something else in terms of the relationship between levels.” Enter the third level of closure that Rosas and colleagues think is needed to complete the conceptual apparatus: computational closure . For this they have turned to computational mechanics, a discipline pioneered by Crutchfield .

Crutchfield introduced a conceptual device called the epsilon (ε) machine. This device can exist in some finite set of states and can predict its own future state on the basis of its current one. It’s a bit like an elevator, said Rosas; an input to the machine, like pressing a button, will cause the machine to transition to a different state (floor) in a deterministic way that depends on its past history—namely, its current floor, whether it’s going up or down, and which other buttons were pressed already. Of course an elevator has myriad component parts, but you don’t need to think about them. Likewise, an ε-machine is an optimal way to represent how unspecified interactions between component parts “compute”—or, one might say, cause—the machine’s future state.

Computational mechanics allows the web of interactions between a complex system’s components to be reduced to the simplest description, called its causal state. The state of the complex system at any moment, which includes information about its past states, produces a distribution of possible future states. Whenever two or more such present states have the same distribution of possible futures, they are said to be in the same causal state. Our brains will never twice have exactly the same firing pattern of neurons, but there are plenty of circumstances where nevertheless we’ll end up doing the same thing.

Rosas and colleagues considered a generic complex system as a set of ε-machines working at different scales. One of these might, say, represent all the molecular-scale ions, ion channels, and so forth that produce currents in our neurons; another represents the firing patterns of the neurons themselves; another, the activity seen in compartments of the brain such as the hippocampus and frontal cortex. The system (here the brain) evolves at all those levels, and in general the relationship between these ε-machines is complicated. But for an emergent system that is computationally closed, the machines at each level can be constructed by coarse-graining the components on just the level below: They are, in the researchers’ terminology, “strongly lumpable.” We might, for example, imagine lumping all the dynamics of the ions and neurotransmitters moving in and out of a neuron into a representation of whether the neuron fires or not. In principle, one could imagine all kinds of different “lumpings” of this sort, but the system is only computationally closed if the ε-machines that represent them are coarse-grained versions of each other in this way. “There is a nestedness” to the structure, Rosas said.

A highly compressed description of the system then emerges at the macro level that captures those dynamics of the micro level that matter to the macroscale behavior—filtered, as it were, through the nested web of intermediate ε-machines. In that case, the behavior of the macro level can be predicted as fully as possible using only macroscale information—there is no need to refer to finer-scale information. It is, in other words, fully emergent. The key characteristic of this emergence, the researchers say, is this hierarchical structure of “strongly lumpable causal states.”

Leaky Emergence

The researchers tested their ideas by seeing what they reveal about a range of emergent behaviors in some model systems. One is a version of a random walk, where some agent wanders around haphazardly in a network that could represent, for example, the streets of a city. A city often exhibits a hierarchy of scales, with densely connected streets within neighborhoods and much more sparsely connected streets between neighborhoods. The researchers find that the outcome of a random walk through such a network is highly lumpable. That is, the probability of the wanderer starting in neighborhood A and ending up in neighborhood B—the macroscale behavior—remains the same regardless of which streets within A or B the walker randomly traverses.

The researchers also considered artificial neural networks like those used in machine-learning and artificial-intelligence algorithms. Some of these networks organize themselves into states that can reliably identify macroscopic patterns in data regardless of microscopic differences between the states of individual neurons in the network. The decision of which pattern will be output by the network “works at a higher level,” said Rosas.

Anil Seth, a neuroscientist at the University of Sussex who studies consciousness, conducts an experiment on perception.

Would Rosas’ scheme help to understand the emergence of robust, large-scale structure in a case like Jupiter’s Great Red Spot? The huge vortex “might satisfy computational closure” Rosas said, “but we’d need to do a proper analysis before being able to claim anything.”

As for living organisms, they seem sometimes to be emergent but sometimes more “vertically integrated,” where microscopic changes do influence large-scale behavior. Consider, for example, a heart. Despite considerable variations in the details of which genes are being expressed, and how much, or what the concentrations of proteins are from place to place, all of our heart muscle cells seem to work in essentially the same way, enabling them to function en masse as a pump driven by coherent, macroscopic electrical pulses passing through the tissue. But it’s not always this way. While many of our genes carry mutations that make no difference to our health, sometimes a mutation—just one genetic “letter” in a DNA sequence that is “wrong”—can be catastrophic. So the independence of the macro from the micro is not complete: There is some leakage between levels. Rosas wonders if living organisms are in fact optimized by allowing for such “leaky” partial emergence—because in life, sometimes it is essential for the macro to heed the details of the micro.

Emergent Causes

Rosas’ framework could help complex systems researchers see when they can and can’t hope to develop predictive coarse-grained models. When a system meets the key requirement of being computationally closed, “you don’t lose any faithfulness by simulating the upper levels and neglecting the lower levels,” he said. But ultimately Rosas hopes an approach like his might answer some deep questions about the structure of the universe—why, for example, life seems to exist only at scales intermediate between the atomic and the galactic.

The framework also has implications for understanding the tricky question of cause and effect in complex and emergent systems. Traditionally, causation has been assumed to flow from the bottom up: Our choices and actions, for example, are ultimately attributed to those firing patterns of our neurons, which in turn are caused by flows of ions across cell membranes.

But in an emergent system, this is not necessarily so; causation can operate at a higher level independently from lower-level details. Rosas’ new computational framework seems to capture this aspect of emergence, which was also explored in earlier work. In 2013, neuroscientist Giulio Tononi of the University of Wisconsin, Madison, working with Erik Hoel and Larissa Albantakis (also at Wisconsin), claimed that, according to a particular measure of causal influence called effective information, the overall behavior of some complex systems is caused more at the higher than the lower levels. This is called causal emergence .

The 2013 work using effective information could have been just a quirk of measuring causal influence this way. But recently, Hoel and neuroscientist Renzo Comolatti have shown that it is not. They took 12 different measures of causal power proposed in the literature and found that with all of them, some complex systems show causal emergence. “It doesn’t matter what measure of causation you pick,” Hoel said. “We just went out into the literature and picked other people’s definitions of causation, and all of them showed causal emergence.” It would be bizarre if this were some chance quirk of all those different measures.

For Hoel, emergent systems are ones whose macroscale behavior has some immunity to randomness or noise at the microscale. For many complex systems, there’s a good chance you can find coarse-grained, macroscopic descriptions that minimize that noise. “It’s that minimization that lies at the heart of a good notion of emergence,” he said.

Tononi says that, while his approach and that of Rosas and colleagues address the same kinds of systems, they have somewhat different criteria for causal emergence. “They define emergence as being when the macro system can predict itself as much as it can be predicted from the micro level,” he said. “But we require more causal information at the macro level than at the micro level.”

The new ideas touch on the issue of free will. While hardened reductionists have argued that there can be no free will because all causation ultimately arises from interactions of atoms and molecules, free will may be rescued by the formalism of higher-level causation. If the main cause of our actions is not our molecules but the emergent mental states that encode memories, intentions, beliefs and so forth, isn’t that enough for a meaningful notion of free will? The new work shows that “there are sensible ways to think about macro-level causation that explain how agents can have a worthwhile form of causal efficacy,” Seth said.

Still, there remains disagreement among researchers about whether macroscopic, agent-level causation can emerge in complex systems. “I’m uncomfortable with this idea that the macroscale can drive the microscale,” said Adami. “The macroscale is just degrees of freedom that you’ve invented.” This is the sort of issue that the scheme proposed by Rosas and colleagues might help to resolve, by burrowing into the mechanics of how different levels of the system speak to one another, and how this conversation must be structured to achieve independence of the macro from the details of the levels below.

At this point, some of the arguments are pretty fuzzy. But Crutchfield is optimistic. “We’ll have this figured out in five or 10 years,” he said. “I really think the pieces are there.”

Original story reprinted with permission from Quanta Magazine , an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

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Key facts about Americans and guns

Guns are deeply ingrained in American society and the nation’s political debates.

The Second Amendment to the United States Constitution guarantees the right to bear arms, and about a third of U.S. adults say they personally own a gun. At the same time, in response to concerns such as  rising gun death rates  and  mass shootings , the U.S. surgeon general has taken the unprecedented step of declaring gun violence a public health crisis .

Here are some key findings about Americans’ views of gun ownership, gun policy and other subjects, drawn from Pew Research Center surveys. 

Pew Research Center conducted this analysis to summarize key facts about Americans’ relationships with guns. We used data from recent Center surveys to provide insights into Americans’ views on gun policy and how those views have changed over time, as well as to examine the proportion of adults who own guns and their reasons for doing so.

The Center survey questions used in this analysis, and more information about the surveys’ methodologies, and can be found at the links in the text.

Measuring gun ownership in the United States comes with unique challenges. Unlike many demographic measures, there is not a definitive data source from the government or elsewhere on how many American adults own guns.

The Pew Research Center survey conducted June 5-11, 2023, on the Center’s American Trends Panel, used two separate questions to measure personal and household ownership. About a third of adults (32%) say they own a gun, while another 10% say they do not personally own a gun but someone else in their household does. These shares have changed little from surveys conducted in  2021  and  2017 . In each of those surveys, 30% reported they owned a gun.

These numbers are largely consistent with  rates of gun ownership reported by Gallup and those reported by  NORC’s General Social Survey .  

The FBI maintains data on background checks on individuals attempting to purchase firearms in the United States. The FBI reported  a surge in background checks  in 2020 and 2021, during the coronavirus pandemic, but FBI statistics show that the number of federal background checks declined in 2022 and 2023. This pattern seems to be continuing so far in 2024. As of June, fewer background checks have been conducted than at the same point in 2023, according to FBI statistics.

About   four-in-ten U.S. adults say they live in a household with a gun, including 32% who say they personally own one,  according to  a Center survey conducted in June 2023 . These numbers are virtually unchanged since the last time we asked this question in 2021.

There are differences in gun ownership rates by political affiliation, gender, community type and other factors.

• Party: 45% of Republicans and GOP-leaning independents say they personally own a gun, compared with 20% of Democrats and Democratic leaners.
• Gender: 40% of men say they own a gun, versus 25% of women.
• Community type: 47% of adults living in rural areas report owning a firearm, as do smaller shares of those who live in suburbs (30%) or urban areas (20%).
• Race and ethnicity: 38% of White Americans own a gun, compared with smaller shares of Black (24%), Hispanic (20%) and Asian (10%) Americans.

Personal protection tops the list of reasons gun owners give for having a firearm.  About seven-in-ten gun owners (72%) say protection is a major reason they own a gun. Considerably smaller shares say that a major reason they own a gun is for hunting (32%), for sport shooting (30%), as part of a gun collection (15%) or for their job (7%). 

Americans’ reasons behind gun ownership have changed only modestly since we fielded a separate survey  about these topics in spring 2017. At that time, 67% of gun owners cited protection as a major reason they had a firearm.

Gun owners tend to have much more positive feelings about having a gun in the house than nonowners who live with them do.  For instance, 71% of gun owners say they enjoy owning a gun – but just 31% of nonowners living in a household with a gun say they enjoy having one in the home. And while 81% of gun owners say owning a gun makes them feel safer, a narrower majority of nonowners in gun households (57%) say the same. Nonowners are also more likely than owners to worry about having a gun at home (27% vs. 12%).

Feelings about gun ownership also differ by political affiliation, even among those who personally own a firearm. Republican gun owners are more likely than Democratic owners to say owning one gives them feelings of safety and enjoyment, while Democratic owners are more likely to say they worry about having a gun in the home.

Non-gun owners are split on whether they see themselves owning a firearm in the future.  About half of Americans who don’t own a gun (52%) say they could never see themselves owning one, while nearly as many (47%) could imagine themselves as gun owners in the future.

Among those who currently do not own a gun, attitudes about owning one in the future differ by party and other factors.

• Party: 61% of Republicans who don’t own a gun say they could see themselves owning one in the future, compared with 40% of Democrats.
• Gender: 56% of men who don’t own a gun say they could see themselves owning one someday; 40% of women nonowners say the same.
• Race and ethnicity: 56% of Black nonowners say they could see themselves owning a gun one day, compared with smaller shares of White (48%), Hispanic (40%) and Asian (38%) nonowners.

A majority of Americans (61%) say it is too easy to legally obtain a gun in this country, according to the June 2023 survey. Far fewer (9%) say it is too hard, while another 30% say it’s about right.

Non-gun owners are nearly twice as likely as gun owners to say it is too easy to legally obtain a gun (73% vs. 38%). Gun owners, in turn, are more than twice as likely as nonowners to say the ease of obtaining a gun is about right (48% vs. 20%).

There are differences by party and community type on this question, too. While 86% of Democrats say it is too easy to obtain a gun legally, far fewer Republicans (34%) say the same. Most urban (72%) and suburban (63%) residents say it’s too easy to legally obtain a gun, but rural residents are more divided: 47% say it is too easy, 41% say it is about right and 11% say it is too hard.

About six-in-ten U.S. adults (58%) favor stricter gun laws. Another 26% say that U.S. gun laws are about right, while 15% favor less strict gun laws.

There   is broad partisan agreement on some gun policy proposals, but most are politically divisive. Majorities of U.S. adults in both partisan coalitions somewhat or strongly favor two policies that would restrict gun access: preventing those with mental illnesses from purchasing guns (88% of Republicans and 89% of Democrats support this) and increasing the minimum age for buying guns to 21 years old (69% of Republicans, 90% of Democrats). Majorities in both parties also  oppose  allowing people to carry concealed firearms without a permit (60% of Republicans and 91% of Democrats oppose this).

Republicans and Democrats differ on several other proposals. While 85% of Democrats favor banning both assault-style weapons and high-capacity ammunition magazines that hold more than 10 rounds, majorities of Republicans oppose  these proposals (57% and 54%, respectively).

Most Republicans, on the other hand, support allowing teachers and school officials to carry guns in K-12 schools (74%) and allowing people to carry concealed guns in more places (71%). These proposals are supported by just 27% and 19% of Democrats, respectively.

The public remains closely divided over whether it’s more important to protect gun rights or control gun ownership, according to an April 2024 survey . Overall, 51% of U.S. adults say it’s more important to protect the right of Americans to own guns, while a similar share (48%) say controlling gun ownership is more important.

Views have shifted slightly since 2022, when we last asked this question. That year, 47% of adults prioritized protecting Americans’ rights to own guns, while 52% said controlling gun ownership was more important.

Views on this topic differ sharply by party. In the most recent survey, 83% of Republicans say protecting gun rights is more important, while 79% of Democrats prioritize controlling gun ownership.

Americans are slightly more likely to say gun ownership does more to increase safety than to decrease it.  Around half of Americans (52%) say gun ownership does more to increase safety by allowing law-abiding citizens to protect themselves, while a slightly smaller share (47%) say gun ownership does more to reduce safety by giving too many people access to firearms and increasing misuse. Views were evenly divided (49% vs. 49%) when we last asked in 2023.

Republicans and Democrats differ widely on this question: 81% of Republicans say gun ownership does more to increase safety, while 74% of Democrats say it does more to reduce safety.

Rural and urban Americans also have starkly different views. Among adults who live in rural areas, 64% say gun ownership increases safety, while among those in urban areas, 57% say it  reduces  safety. Those living in the suburbs are about evenly split in their views.

More than half of U.S. adults say an increase in the number of guns in the country is bad for society, according to the April 2024 survey. Some 54% say, generally, this is very or somewhat bad for society. Another 21% say it is very or somewhat good for society, and a quarter say it is neither good nor bad for society.

About half of Americans (49%) see gun violence as a major problem,  according to a May 2024 survey. This is down from 60% in June 2023, but roughly on par with views in previous years. In the more recent survey, 27% say gun violence is a moderately big problem, and about a quarter say it is either a small problem (19%) or not a problem at all (4%).

A majority of public K-12 teachers (59%) say they are at least somewhat worried about the possibility of a shooting ever happening at their school, including 18% who are very or extremely worried, according to a fall 2023 Center survey of teachers . A smaller share of teachers (39%) say they are not too or not at all worried about a shooting occurring at their school.

School shootings are a concern for K-12 parents as well: 32% say they are very or extremely worried about a shooting ever happening at their children’s school, while 37% are somewhat worried, according to  a fall 2022 Center survey of parents with at least one child younger than 18 who is not homeschooled. Another 31% of K-12 parents say they are not too or not at all worried about this.

Note: This is an update of a post originally published on Jan. 5, 2016 .

• Partisanship & Issues
• Political Issues

Katherine Schaeffer is a research analyst at Pew Research Center .

Americans’ Extreme Weather Policy Views and Personal Experiences

U.s. adults under 30 have different foreign policy priorities than older adults, many adults in east and southeast asia support free speech, are open to societal change, nato seen favorably in member states; confidence in zelenskyy down in europe, u.s., same-sex marriage around the world, most popular.

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ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center

What a dietitian who avoids ultra-processed foods would order at Chipotle

• Ultra-processed foods have been linked to a range of health problems. 
• Dietitian Kat Garcia-Benson avoids UPFs but focuses on "what to add versus what to take away" from meals.
• Garcia-Benson would order a meal with steak and black beans at Chipotle.

A dietitian who avoids ultra-processed foods shared with Business Insider what she would order at Chipotle.

The Mexican-style fast-casual spot is popular among city workers, including finance bros who work long hours. The chain has even launched a limited edition "Chipotle Boy" bowl as a nod to these devoted patrons.

Busy people who rely on fast food joints may worry that they are unwittingly eating ultra-processed foods — which are linked to a host of health problems — as it can be hard to check the ingredients of meals.

The best approach when eating out is to prioritize lean protein and fiber and minimize foods that aren't so nutrient-dense, dietitian Kat Garcia-Benson said.

We can prioritize nutritious food while considering what's available and accessible, she said: "I like to focus on what to add versus what to take away."

In that vein, three dietitians previously shared what they would order from Chipotle for a high-protein meal .

Garcia-Benson shared what she would order at Chipotle if she were grabbing a quick lunch.

Dietitian Garcia Benson's Chipotle order:

• Burrito or salad bowl
• Romaine lettuce
• Black beans
• Fajita veggies

Pick a burrito on days you need extra energy

Garcia-Benson would choose either the burrito bowl (which comes with rice) or the salad bowl (which has a lettuce base), depending on what she was doing for the rest of the day, how much energy it required, and how hungry she was.

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"If I need more carbohydrates, if I need it to be more filling, or if I'm going to work out a couple hours later, then I would get just a regular bowl that has rice versus the salad that won't," she said.

Lean protein and fiber

At Chipotle, she would go for the steak mainly because it's her personal preference.

She also opts for black beans for added protein and fiber. Beans are a great source of fiber and are a staple in the diet of many Blue Zones , areas of the world where residents live around 10 years longer than the country's average life expectancy.

She chooses brown rice over white as it contains more fiber, and fajita vegetables for micronutrients and even more fiber.

Healthy fats

If you're eating out, you probably don't need to worry too much about adding fat to your meal because it'll be there for taste.

"That's the goal of restaurants, to make it taste good, right?" Garcia-Benson said.

But if she's getting a salad bowl, she might add guacamole for extra healthy fats or to make her dish more filling.

As well as healthy fats, avocados contain fiber, vitamins, and antioxidants.

Watch: A British tourist and a local find the best birria tacos in Los Angeles

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Biden- ⁠ Harris Administration Takes Actions to Advance Kids’ Online Health, Safety, and   Privacy

America is experiencing an unprecedented youth mental health crisis. In the United States, 95% of teenagers use social media, and nearly a third of teens report using social media almost constantly. At the same time, the number of children and young adults with anxiety and depression has risen nearly 30% in recent years. According to the CDC, in 2021, nearly 3 in 5 teen girls reported feeling persistently sad or hopeless , the highest level reported over the past decade, and 1 in 3 had seriously considered suicide.

While online platforms facilitate social connection and learning, they also pose a range of harms. Children and youth, particularly Black, Brown, and LGBTQIA+ youth, face harassment, cyberbullying, exposure to harmful content, and sexual exploitation and abuse at disproportionate rates. Nearly half of teens have experienced some form of cyberbullying, including harassment and image-based sexual abuse—which has skyrocketed in recent years with the advent of AI and disproportionately impacts girls. The numbers are staggering – and that’s why the Biden-Harris Administration has taken bold action to protect children online.

President Biden has repeatedly called on Congress to pass stronger protections for children’s online health, privacy, and safety, and has made tackling the youth mental health crisis a top priority for the Biden-Harris Administration. To advance this work, President Biden created a task force for agencies across the U.S. government to mobilize to advance online health, safety, and privacy and address these modern threats facing youth.

Today, the Biden-Harris Administration convened U.S. officials, civil society leaders, youth advocates, academic researchers and other experts to announce new actions that will advance children’s online health, safety, and privacy:

The Kids Online Health and Safety Task Force unveiled its final report “ Best Practices for Families and Guidance for Industry .”

• The best practices and guidance reflect input from youth, parents and experts from across the nation. The report identifies key risks and benefits of online platforms and digital technologies to young people’s health, safety and privacy and provides best practices for parents and caregivers, recommended practices for companies, and a research agenda to support further study into online harms and their impacts on children and youth wellbeing. The report is accompanied by a set of next steps for policymakers, including calls to pass bipartisan federal legislation and require access to platform data for independent researchers in privacy-preserving ways.

The White House Office of Science and Technology Policy, in collaboration with the National Telecommunications and Information Administration (NTIA), is announcing it will work with stakeholders to draft guidelines for researchers working with online platform data.

• The guidelines will be a reference for researchers, companies implementing new researcher access programs, bodies generating international research agreements, and legislators as they draft privacy laws.

The Centers for Disease Control and Prevention are announcing the development of new ways to measure and study student use of social media, and resources for addressing children’s online safety.

• For the first time this fall, the 2023 Youth Risk Behavior Survey will include a national estimate of social media use among high school students. This report provides the most recent surveillance data and 10-year trends on health behaviors and experiences among high school students in the United States.
• The CDC recently developed and updated resources for children’s online safety, including the Youth Violence Prevention Toolkit , two guides for LGBTQ youth and their parents and caregivers on healthy, safe relationships, and StopBullying.gov with resources on cyberbullying.

The Substance Abuse and Mental Health Administration is releasing new guidance for health providers on social media and mental health.

• The SAMHSA-funded American Academy of Pediatrics Center of Excellence for Social Media and Youth Mental Health will produce new clinical case examples that focus on diverse adolescent patients and demonstrate how to integrate conversations about media use into health consultations. These will be tailored towards pediatricians and other clinicians who work with children and families.

The National Institute of Standards and Technology (NIST) will evaluate new ways to estimate and verify age online and to ensure equity across ages and genders.

• As detailed in their May 2024 report, NIST has new plans for a long-term effort to perform frequent, regular tests of age estimation and verification algorithms. The agency created a new evaluation system for age estimation algorithms and plans to update its evaluation results every four to six weeks.

The National Institutes of Health (NIH) is developing a new framework for research on youth and technology developed by the National Institute of Child Health and Human Development (NICHD) and National Institute of Mental Health (NIMH).

• NICHD & NIMH are working together to develop a strategic framework for research on youth and technology, anticipated in Winter 2024/2025.

These announcements build upon recent administration actions from across the U.S. government to protect kids online:

The Federal Trade Commission (FTC) has taken robust enforcement action to protect children’s privacy.

• The FTC recently announced an order that will ban NGL Labs, LLC. from offering anonymous messaging apps to kids under 18. The FTC took action to prevent unfair marketing to kids and teens, and prevent the company from deceiving kids into signing up for paid services.
• The FTC is also working to update rules on the Children’s Online Privacy Protection Act (COPPA) to address new harms to children’s privacy posed by digital, new and emerging technologies.

The White House has issued a call to action to combat image-based sexual abuse to encourage voluntary actions to curb this growing harm.

• In May, the Office of Science and Technology Policy and the Gender Policy Council issued a Call to Action to Combat Image-Based Sexual Abuse—including synthetic content generated by artificial intelligence (AI) and real images distributed without consent— which has skyrocketed in recent years, disproportionately targeting girls, women and LGBTQI+ people. Through this call to action, the White House is encouraging companies and other organizations to provide meaningful tools that will prevent and mitigate harms, and to limit websites and mobile apps whose primary business is to create, facilitate, monetize, or disseminate image-based sexual abuse.

The Department of Homeland Security (DHS) launched public awareness and other efforts to combat child sexual exploitation.

• In April 2024, DHS announced Know2Protect, Together We Can Stop Online Child Exploitation™, a national public awareness campaign to raise awareness of child exploitation and how to keep children safe. Through partnerships with sports leagues, technology companies, and youth-serving organizations, the program is reaching kids and families across the country and has received more than 100 million impressions online. Project iGuardian, the official educational program of the Know2Protect campaign, has given 950 presentations to more than 82,000 kids, teens, parents, and teachers, which has yielded more than 41 victim disclosures and 72 investigative leads for online child sexual exploitation and abuse.

The Department of Education released new guidance for educational technology developers. 

• The Department of Education’s new guidance will help to protect students and ensure the development of safe, responsible, and nondiscriminatory uses of AI and education technologies used by children and students across the nation.

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