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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."

13 Different Types of Hypothesis

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

There are 13 different types of hypothesis. These include simple, complex, null, alternative, composite, directional, non-directional, logical, empirical, statistical, associative, exact, and inexact.

A hypothesis can be categorized into one or more of these types. However, some are mutually exclusive and opposites. Simple and complex hypotheses are mutually exclusive, as are direction and non-direction, and null and alternative hypotheses.

Below I explain each hypothesis in simple terms for absolute beginners. These definitions may be too simple for some, but they’re designed to be clear introductions to the terms to help people wrap their heads around the concepts early on in their education about research methods .

Types of Hypothesis

Before you Proceed: Dependent vs Independent Variables

A research study and its hypotheses generally examine the relationships between independent and dependent variables – so you need to know these two concepts:

• The independent variable is the variable that is causing a change.
• The dependent variable is the variable the is affected by the change. This is the variable being tested.

Read my full article on dependent vs independent variables for more examples.

Example: Eating carrots (independent variable) improves eyesight (dependent variable).

1. Simple Hypothesis

A simple hypothesis is a hypothesis that predicts a correlation between two test variables: an independent and a dependent variable.

This is the easiest and most straightforward type of hypothesis. You simply need to state an expected correlation between the dependant variable and the independent variable.

You do not need to predict causation (see: directional hypothesis). All you would need to do is prove that the two variables are linked.

Simple Hypothesis Examples

2. Complex Hypothesis

A complex hypothesis is a hypothesis that contains multiple variables, making the hypothesis more specific but also harder to prove.

You can have multiple independent and dependant variables in this hypothesis.

Complex Hypothesis Example

In the above example, we have multiple independent and dependent variables:

• Independent variables: Age and weight.
• Dependent variables: diabetes and heart disease.

Because there are multiple variables, this study is a lot more complex than a simple hypothesis. It quickly gets much more difficult to prove these hypotheses. This is why undergraduate and first-time researchers are usually encouraged to use simple hypotheses.

3. Null Hypothesis

A null hypothesis will predict that there will be no significant relationship between the two test variables.

For example, you can say that “The study will show that there is no correlation between marriage and happiness.”

A good way to think about a null hypothesis is to think of it in the same way as “innocent until proven guilty”[1]. Unless you can come up with evidence otherwise, your null hypothesis will stand.

A null hypothesis may also highlight that a correlation will be inconclusive . This means that you can predict that the study will not be able to confirm your results one way or the other. For example, you can say “It is predicted that the study will be unable to confirm a correlation between the two variables due to foreseeable interference by a third variable .”

Beware that an inconclusive null hypothesis may be questioned by your teacher. Why would you conduct a test that you predict will not provide a clear result? Perhaps you should take a closer look at your methodology and re-examine it. Nevertheless, inconclusive null hypotheses can sometimes have merit.

Null Hypothesis Examples

4. Alternative Hypothesis

An alternative hypothesis is a hypothesis that is anything other than the null hypothesis. It will disprove the null hypothesis.

We use the symbol H A or H 1 to denote an alternative hypothesis.

The null and alternative hypotheses are usually used together. We will say the null hypothesis is the case where a relationship between two variables is non-existent. The alternative hypothesis is the case where there is a relationship between those two variables.

The following statement is always true: H 0 ≠ H A .

Let’s take the example of the hypothesis: “Does eating oatmeal before an exam impact test scores?”

We can have two hypotheses here:

• Null hypothesis (H 0 ): “Eating oatmeal before an exam does not impact test scores.”
• Alternative hypothesis (H A ): “Eating oatmeal before an exam does impact test scores.”

For the alternative hypothesis to be true, all we have to do is disprove the null hypothesis for the alternative hypothesis to be true. We do not need an exact prediction of how much oatmeal will impact the test scores or even if the impact is positive or negative. So long as the null hypothesis is proven to be false, then the alternative hypothesis is proven to be true.

5. Composite Hypothesis

A composite hypothesis is a hypothesis that does not predict the exact parameters, distribution, or range of the dependent variable.

Often, we would predict an exact outcome. For example: “23 year old men are on average 189cm tall.” Here, we are giving an exact parameter. So, the hypothesis is not composite.

But, often, we cannot exactly hypothesize something. We assume that something will happen, but we’re not exactly sure what. In these cases, we might say: “23 year old men are not on average 189cm tall.”

We haven’t set a distribution range or exact parameters of the average height of 23 year old men. So, we’ve introduced a composite hypothesis as opposed to an exact hypothesis.

Generally, an alternative hypothesis (discussed above) is composite because it is defined as anything except the null hypothesis. This ‘anything except’ does not define parameters or distribution, and therefore it’s an example of a composite hypothesis.

6. Directional Hypothesis

A directional hypothesis makes a prediction about the positivity or negativity of the effect of an intervention prior to the test being conducted.

Instead of being agnostic about whether the effect will be positive or negative, it nominates the effect’s directionality.

We often call this a one-tailed hypothesis (in contrast to a two-tailed or non-directional hypothesis) because, looking at a distribution graph, we’re hypothesizing that the results will lean toward one particular tail on the graph – either the positive or negative.

Directional Hypothesis Examples

7. Non-Directional Hypothesis

A non-directional hypothesis does not specify the predicted direction (e.g. positivity or negativity) of the effect of the independent variable on the dependent variable.

These hypotheses predict an effect, but stop short of saying what that effect will be.

A non-directional hypothesis is similar to composite and alternative hypotheses. All three types of hypothesis tend to make predictions without defining a direction. In a composite hypothesis, a specific prediction is not made (although a general direction may be indicated, so the overlap is not complete). For an alternative hypothesis, you often predict that the even will be anything but the null hypothesis, which means it could be more or less than H 0 (or in other words, non-directional).

Let’s turn the above directional hypotheses into non-directional hypotheses.

Non-Directional Hypothesis Examples

8. Logical Hypothesis

A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions.

These are most commonly used in philosophy because philosophical questions are often untestable and therefore we must rely on our logic to formulate logical theories.

Usually, we would want to turn a logical hypothesis into an empirical one through testing if we got the chance. Unfortunately, we don’t always have this opportunity because the test is too complex, expensive, or simply unrealistic.

Here are some examples:

• Before the 1980s, it was hypothesized that the Titanic came to its resting place at 41° N and 49° W, based on the time the ship sank and the ship’s presumed path across the Atlantic Ocean. However, due to the depth of the ocean, it was impossible to test. Thus, the hypothesis was simply a logical hypothesis.
• Dinosaurs closely related to Aligators probably had green scales because Aligators have green scales. However, as they are all extinct, we can only rely on logic and not empirical data.

9. Empirical Hypothesis

An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a ‘working hypothesis’.

We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments. Empirical research relies on tests that can be verified by observation and measurement.

So, an empirical hypothesis is a hypothesis that can and will be tested.

• Raising the wage of restaurant servers increases staff retention.
• Adding 1 lb of corn per day to cows’ diets decreases their lifespan.
• Mushrooms grow faster at 22 degrees Celsius than 27 degrees Celsius.

Each of the above hypotheses can be tested, making them empirical rather than just logical (aka theoretical).

10. Statistical Hypothesis

A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations.

It requires the use of datasets or carefully selected representative samples so that statistical inference can be drawn across a larger dataset.

This type of research is necessary when it is impossible to assess every single possible case. Imagine, for example, if you wanted to determine if men are taller than women. You would be unable to measure the height of every man and woman on the planet. But, by conducting sufficient random samples, you would be able to predict with high probability that the results of your study would remain stable across the whole population.

You would be right in guessing that almost all quantitative research studies conducted in academic settings today involve statistical hypotheses.

Statistical Hypothesis Examples

• Human Sex Ratio. The most famous statistical hypothesis example is that of John Arbuthnot’s sex at birth case study in 1710. Arbuthnot used birth data to determine with high statistical probability that there are more male births than female births. He called this divine providence, and to this day, his findings remain true: more men are born than women.
• Lady Testing Tea. A 1935 study by Ronald Fisher involved testing a woman who believed she could tell whether milk was added before or after water to a cup of tea. Fisher gave her 4 cups in which one randomly had milk placed before the tea. He repeated the test 8 times. The lady was correct each time. Fisher found that she had a 1 in 70 chance of getting all 8 test correct, which is a statistically significant result.

11. Associative Hypothesis

An associative hypothesis predicts that two variables are linked but does not explore whether one variable directly impacts upon the other variable.

We commonly refer to this as “ correlation does not mean causation ”. Just because there are a lot of sick people in a hospital, it doesn’t mean that the hospital made the people sick. There is something going on there that’s causing the issue (sick people are flocking to the hospital).

So, in an associative hypothesis, you note correlation between an independent and dependent variable but do not make a prediction about how the two interact. You stop short of saying one thing causes another thing.

Associative Hypothesis Examples

• Sick people in hospital. You could conduct a study hypothesizing that hospitals have more sick people in them than other institutions in society. However, you don’t hypothesize that the hospitals caused the sickness.
• Lice make you healthy. In the Middle Ages, it was observed that sick people didn’t tend to have lice in their hair. The inaccurate conclusion was that lice was not only a sign of health, but that they made people healthy. In reality, there was an association here, but not causation. The fact was that lice were sensitive to body temperature and fled bodies that had fevers.

12. Causal Hypothesis

A causal hypothesis predicts that two variables are not only associated, but that changes in one variable will cause changes in another.

A causal hypothesis is harder to prove than an associative hypothesis because the cause needs to be definitively proven. This will often require repeating tests in controlled environments with the researchers making manipulations to the independent variable, or the use of control groups and placebo effects .

If we were to take the above example of lice in the hair of sick people, researchers would have to put lice in sick people’s hair and see if it made those people healthier. Researchers would likely observe that the lice would flee the hair, but the sickness would remain, leading to a finding of association but not causation.

Causal Hypothesis Examples

13. Exact vs. Inexact Hypothesis

For brevity’s sake, I have paired these two hypotheses into the one point. The reality is that we’ve already seen both of these types of hypotheses at play already.

An exact hypothesis (also known as a point hypothesis) specifies a specific prediction whereas an inexact hypothesis assumes a range of possible values without giving an exact outcome. As Helwig [2] argues:

“An “exact” hypothesis specifies the exact value(s) of the parameter(s) of interest, whereas an “inexact” hypothesis specifies a range of possible values for the parameter(s) of interest.”

Generally, a null hypothesis is an exact hypothesis whereas alternative, composite, directional, and non-directional hypotheses are all inexact.

See Next: 15 Hypothesis Examples

This is introductory information that is basic and indeed quite simplified for absolute beginners. It’s worth doing further independent research to get deeper knowledge of research methods and how to conduct an effective research study. And if you’re in education studies, don’t miss out on my list of the best education studies dissertation ideas .

[1] https://jnnp.bmj.com/content/91/6/571.abstract

[2] http://users.stat.umn.edu/~helwig/notes/SignificanceTesting.pdf

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

2 thoughts on “13 Different Types of Hypothesis”

Wow! This introductionary materials are very helpful. I teach the begginers in research for the first time in my career. The given tips and materials are very helpful. Chris, thank you so much! Excellent materials!

You’re more than welcome! If you want a pdf version of this article to provide for your students to use as a weekly reading on in-class discussion prompt for seminars, just drop me an email in the Contact form and I’ll get one sent out to you.

When I’ve taught this seminar, I’ve put my students into groups, cut these definitions into strips, and handed them out to the groups. Then I get them to try to come up with hypotheses that fit into each ‘type’. You can either just rotate hypothesis types so they get a chance at creating a hypothesis of each type, or get them to “teach” their hypothesis type and examples to the class at the end of the seminar.

Cheers, Chris

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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

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How to Formulate a Hypothesis: Example and Explanation

A hypothesis is a smart guess about how things work. It helps scientists figure out what they think will happen in their experiments. Making a good hypothesis is important because it guides the research and helps find answers to questions. In this article, we will learn how to make a strong hypothesis, look at some examples, and understand why they matter.

Key Takeaways

• A hypothesis is an educated guess that can be tested through experiments.
• Good hypotheses are clear, precise, and can be proven wrong.
• There are different types of hypotheses, like simple, complex, null, and alternative.
• Variables play a big role in forming a hypothesis, including independent, dependent, and control variables.
• Testing and refining hypotheses are crucial steps in scientific research.

Understanding the Concept of a Hypothesis

Definition and importance.

A hypothesis is an idea you can test. It's a clear statement predicting the outcome of your study. It's not just a guess ; it should be based on what you already know. A good hypothesis helps you focus your research and guides your experiments.

Role in Scientific Research

In science, a hypothesis is very important. It gives you a starting point for your experiments. You can test it to see if it's true or false. This helps you understand more about the world. A clear, testable hypothesis is key to good research .

Common Misconceptions

Many people think a hypothesis is just a wild guess. This is not true. A hypothesis is based on existing knowledge and theories. Another common mistake is making the hypothesis too broad. A good hypothesis should be specific and testable.

Steps to Formulate a Hypothesis

Formulating a hypothesis is a critical step in the scientific method. It involves several key stages that help ensure your hypothesis is both testable and relevant to your research question. Here are the steps you should follow:

Gathering Observations

Start by collecting as many observations about your topic or problem as possible. These observations will form the foundation of your hypothesis. Good clinical research starts from a plausible hypothesis supported by contemporary scientific knowledge. Look for patterns or trends in the data that might suggest a possible explanation.

Identifying Variables

Next, identify the variables involved in your study. Variables are the elements that you will measure or manipulate in your research. There are typically three types of variables: independent, dependent, and control variables. Understanding these will help you design a more effective experiment.

Developing Possible Explanations

Once you have gathered your observations and identified your variables, the next step is to develop possible explanations for the patterns you have observed. This is where you start to formulate your hypothesis. Think of ways to confirm or disprove each possible explanation through experimentation. This process is known as falsifiability and is crucial for a robust hypothesis.

Characteristics of a Good Hypothesis

Testability and falsifiability.

A good hypothesis must be testable, meaning you can design an experiment to check if it's true or false. Testability is crucial because it allows you to gather evidence to support or refute your hypothesis. Additionally, a hypothesis should be falsifiable, which means there should be a possible outcome that can prove it wrong. This aligns with the falsification principle proposed by Karl Popper, which is fundamental in scientific research.

Clarity and Precision

Your hypothesis should be clear and precise, avoiding any vague language. This clarity helps in demystifying the concept of a thesis statement . A well-defined hypothesis makes it easier to design experiments and interpret results. For example, instead of saying "Plants grow better with more light," you could say, "If plants receive 8 hours of sunlight daily, then they will grow taller than plants that receive 4 hours of sunlight daily."

Relevance to Research Question

A good hypothesis should be directly related to your research question. It should provide a clear direction for your study and help you focus on specific variables. This relevance ensures that your hypothesis is not just a random guess but is grounded in existing knowledge and observations. Hypotheses have strong, arguably foundational, utility as a tool of science . They support the falsification principle, proposed by Karl Popper as fundamental in scientific research.

Types of Hypotheses in Research

When conducting research, it's crucial to understand the different types of hypotheses you might encounter. Each type serves a unique purpose and helps guide your study in specific ways. Knowing these types can enhance the clarity and focus of your research proposal .

Examples of Hypotheses

Simple hypothesis examples.

A simple hypothesis suggests a relationship between two variables: one independent and one dependent. For instance, "If students sleep for at least 8 hours, then their test scores will improve." This type of hypothesis is straightforward and easy to test.

Complex Hypothesis Examples

A complex hypothesis involves more than two variables. An example could be, "If students sleep for at least 8 hours and eat a healthy breakfast, then their test scores and overall well-being will improve." This type of hypothesis examines multiple factors and their combined effects.

Null and Alternative Hypothesis Examples

The null hypothesis states that there is no relationship between the variables. For example, "There is no difference in test scores between students who sleep for 8 hours and those who do not." The alternative hypothesis, on the other hand, suggests a relationship: "Students who sleep for 8 hours will have better test scores than those who do not."

Understanding these examples helps clarify how to structure your own hypotheses. Whether simple or complex, each type plays a crucial role in scientific research.

The Role of Variables in Hypothesis Formulation

When formulating a hypothesis, understanding the role of variables is crucial. Variables are the elements that you measure or manipulate in your research . They help you establish relationships and test your predictions effectively.

Testing Your Hypothesis

Designing experiments.

Designing an experiment is a crucial step in testing your hypothesis. A well-designed experiment ensures that you can accurately test your hypothesis and obtain reliable results. Start by defining your independent and dependent variables clearly. Make sure to control other factors that might influence the outcome. This is essential for maintaining the integrity of your experiment. You should also consider the ethical implications of your experiment to ensure it adheres to accepted standards.

Data Collection Methods

Once your experiment is designed, the next step is to collect data. Choose data collection methods that are appropriate for your research question and hypothesis. Common methods include surveys, observations, and experiments. Ensure that your data collection process is systematic and consistent to avoid any biases. Remember, the goal is to gather data that will either support or refute your hypothesis.

Analyzing Results

After collecting your data, the next step is to analyze the results. Use statistical methods to determine whether your data supports your hypothesis. This involves calculating the likelihood that your results are due to chance. If your data does not support your hypothesis, don't be discouraged. Unexpected findings can lead to new questions and further research. Always be open to conducting further experiments to validate and understand your findings.

Common Pitfalls in Hypothesis Formulation

When formulating a hypothesis, it's crucial to avoid common mistakes that can undermine your research. Here are some pitfalls to watch out for:

Overly Broad Hypotheses

One of the most frequent errors is creating a hypothesis that is too broad. A broad hypothesis can be difficult to test and may not provide meaningful results. Narrowing down your hypothesis to a specific aspect of your research question can make it more manageable and testable.

Lack of Testability

A hypothesis must be testable to be valid. If you can't design an experiment to test your hypothesis, it's not useful. Ensure that your hypothesis includes variables that can be measured and tested. This is essential for revolutionizing research: the secrets of effective experimental design .

Ignoring Alternative Explanations

Another common mistake is failing to consider other possible explanations for your observations. When you ignore alternative explanations, you risk missing out on important insights. Always evaluate assumptions, revise methodology, and consider alternative explanations to strengthen your hypothesis.

By being aware of these pitfalls, you can create a more robust and reliable hypothesis for your research.

Refining and Revising Hypotheses

When you conduct research, it’s common to find that your initial hypothesis may not hold true. This is a normal part of the scientific process. If your results do not support your original hypothesis, consider suggesting alternative options for future studies. This can help guide further research and improve understanding of the topic.

To ensure your hypothesis is strong, you can use a checklist to identify any weaknesses. Here are some questions to consider:

• Is the hypothesis clear and specific?
• Can it be tested through experiments?
• Does it relate to the research question?

By answering these questions, you can refine your hypothesis and make it more robust. Additionally, incorporating feedback from peers can provide new insights and help you adjust your hypothesis based on new data.

In summary, refining and revising your hypothesis is essential for advancing your research. It allows you to adapt to new findings and improve the clarity and focus of your work. Remember, the goal is to develop a hypothesis that can lead to meaningful conclusions and further exploration in your field.

In the context of educational research, a recent meta-analysis highlights the importance of understanding the relationship between psychological needs and student well-being. This shows how refining hypotheses can lead to better insights into complex issues. Similarly, a grounded theory study emphasizes the need for thorough reviews to identify key issues in research, which can also inform hypothesis revision.

Case Studies of Hypothesis Formulation

One of the most famous historical examples of hypothesis formulation is Gregor Mendel's work on pea plants. Mendel's hypothesis about inheritance patterns laid the groundwork for modern genetics. He observed the traits of pea plants and formulated hypotheses about how these traits were passed down through generations. His work is a classic example of how careful observation and hypothesis testing can lead to significant scientific breakthroughs.

In contemporary research, hypothesis formulation continues to play a crucial role. For instance, in the field of psychology, researchers often develop hypotheses to understand human behavior. A recent study on the effects of social media on mental health formulated the hypothesis that increased social media use leads to higher levels of anxiety and depression. This hypothesis was tested through surveys and data analysis, providing valuable insights into the relationship between social media and mental health.

From both historical and contemporary examples, several lessons can be learned about effective hypothesis formulation:

• Observation is key : Careful observation of phenomena is the first step in formulating a hypothesis.
• Clarity and precision : A good hypothesis should be clear and precise, making it easier to test.
• Testability: Ensure that your hypothesis can be tested through experiments or data analysis.
• Flexibility: Be prepared to revise your hypothesis based on new data or feedback.

By understanding these lessons, you can improve your own hypothesis formulation process and contribute to the advancement of scientific knowledge.

In our "Case Studies of Hypothesis Formulation" section, we dive into real-world examples that show how to create strong hypotheses. These case studies are designed to help you understand the process and apply it to your own work. If you're looking for more detailed guidance, visit our website for step-by-step instructions and special offers. Don't miss out on the chance to improve your research skills!

Formulating a hypothesis is a fundamental step in the scientific method that helps guide research and experimentation. By gathering observations, evaluating potential causes, and developing testable statements, researchers can create hypotheses that are both meaningful and falsifiable. This process not only aids in understanding the problem at hand but also in predicting outcomes and drawing conclusions based on empirical evidence. Remember, a well-crafted hypothesis is clear, concise, and provides a direction for future research. With practice and careful consideration, anyone can learn to formulate effective hypotheses that contribute to scientific knowledge.

Frequently Asked Questions

What is a hypothesis.

A hypothesis is an educated guess about how things work. It's a statement that can be tested to see if it's true or false.

Why is a hypothesis important in scientific research?

A hypothesis helps guide your experiments and research. It gives you a clear focus and helps you understand what you're trying to find out.

What are the steps to formulate a good hypothesis?

To create a good hypothesis, start by gathering observations, look for patterns, and identify variables. Then, come up with possible explanations that you can test.

What makes a hypothesis testable?

A testable hypothesis is one that you can prove or disprove through experiments or observations. It should be clear and specific.

Can a hypothesis be proven true?

A hypothesis can be supported by evidence, but it can't be proven true beyond all doubt. New evidence might change our understanding.

What are independent and dependent variables?

Independent variables are the ones you change in an experiment. Dependent variables are the ones you measure to see if they change because of the independent variable.

What is a null hypothesis?

A null hypothesis states that there is no relationship between the variables being studied. It's often used as a starting point for testing.

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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

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

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

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

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

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

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

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Step 1. Ask a question

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

Step 2. Do some preliminary research

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

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

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

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

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

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

 Statistics

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

Research bias

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

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

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

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

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

About the author

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Researcher, Academic Writer, Web developer

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Home Market Research

Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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Hypothesis n., plural: hypotheses [/haɪˈpɑːθəsɪs/] Definition: Testable scientific prediction

Table of Contents

What Is Hypothesis?

A scientific hypothesis is a foundational element of the scientific method . It’s a testable statement proposing a potential explanation for natural phenomena. The term hypothesis means “little theory” . A hypothesis is a short statement that can be tested and gives a possible reason for a phenomenon or a possible link between two variables . In the setting of scientific research, a hypothesis is a tentative explanation or statement that can be proven wrong and is used to guide experiments and empirical research.

It is an important part of the scientific method because it gives a basis for planning tests, gathering data, and judging evidence to see if it is true and could help us understand how natural things work. Several hypotheses can be tested in the real world, and the results of careful and systematic observation and analysis can be used to support, reject, or improve them.

Researchers and scientists often use the word hypothesis to refer to this educated guess . These hypotheses are firmly established based on scientific principles and the rigorous testing of new technology and experiments .

For example, in astrophysics, the Big Bang Theory is a working hypothesis that explains the origins of the universe and considers it as a natural phenomenon. It is among the most prominent scientific hypotheses in the field.

“The scientific method: steps, terms, and examples” by Scishow:

Biology definition: A hypothesis  is a supposition or tentative explanation for (a group of) phenomena, (a set of) facts, or a scientific inquiry that may be tested, verified or answered by further investigation or methodological experiment. It is like a scientific guess . It’s an idea or prediction that scientists make before they do experiments. They use it to guess what might happen and then test it to see if they were right. It’s like a smart guess that helps them learn new things. A scientific hypothesis that has been verified through scientific experiment and research may well be considered a scientific theory .

Etymology: The word “hypothesis” comes from the Greek word “hupothesis,” which means “a basis” or “a supposition.” It combines “hupo” (under) and “thesis” (placing). Synonym:   proposition; assumption; conjecture; postulate Compare:   theory See also: null hypothesis

Characteristics Of Hypothesis

A useful hypothesis must have the following qualities:

• It should never be written as a question.
• You should be able to test it in the real world to see if it’s right or wrong.
• It needs to be clear and exact.
• It should list the factors that will be used to figure out the relationship.
• It should only talk about one thing. You can make a theory in either a descriptive or form of relationship.
• It shouldn’t go against any natural rule that everyone knows is true. Verification will be done well with the tools and methods that are available.
• It should be written in as simple a way as possible so that everyone can understand it.
• It must explain what happened to make an answer necessary.
• It should be testable in a fair amount of time.
• It shouldn’t say different things.

Sources Of Hypothesis

Sources of hypothesis are:

• Patterns of similarity between the phenomenon under investigation and existing hypotheses.
• Insights derived from prior research, concurrent observations, and insights from opposing perspectives.
• The formulations are derived from accepted scientific theories and proposed by researchers.
• In research, it’s essential to consider hypothesis as different subject areas may require various hypotheses (plural form of hypothesis). Researchers also establish a significance level to determine the strength of evidence supporting a hypothesis.
• Individual cognitive processes also contribute to the formation of hypotheses.

One hypothesis is a tentative explanation for an observation or phenomenon. It is based on prior knowledge and understanding of the world, and it can be tested by gathering and analyzing data. Observed facts are the data that are collected to test a hypothesis. They can support or refute the hypothesis.

For example, the hypothesis that “eating more fruits and vegetables will improve your health” can be tested by gathering data on the health of people who eat different amounts of fruits and vegetables. If the people who eat more fruits and vegetables are healthier than those who eat less fruits and vegetables, then the hypothesis is supported.

Hypotheses are essential for scientific inquiry. They help scientists to focus their research, to design experiments, and to interpret their results. They are also essential for the development of scientific theories.

Types Of Hypothesis

In research, you typically encounter two types of hypothesis: the alternative hypothesis (which proposes a relationship between variables) and the null hypothesis (which suggests no relationship).

Simple Hypothesis

It illustrates the association between one dependent variable and one independent variable. For instance, if you consume more vegetables, you will lose weight more quickly. Here, increasing vegetable consumption is the independent variable, while weight loss is the dependent variable.

Complex Hypothesis

It exhibits the relationship between at least two dependent variables and at least two independent variables. Eating more vegetables and fruits results in weight loss, radiant skin, and a decreased risk of numerous diseases, including heart disease.

Directional Hypothesis

It shows that a researcher wants to reach a certain goal. The way the factors are related can also tell us about their nature. For example, four-year-old children who eat well over a time of five years have a higher IQ than children who don’t eat well. This shows what happened and how it happened.

Non-directional Hypothesis

When there is no theory involved, it is used. It is a statement that there is a connection between two variables, but it doesn’t say what that relationship is or which way it goes.

Null Hypothesis

It says something that goes against the theory. It’s a statement that says something is not true, and there is no link between the independent and dependent factors. “H 0 ” represents the null hypothesis.

Associative and Causal Hypothesis

When a change in one variable causes a change in the other variable, this is called the associative hypothesis . The causal hypothesis, on the other hand, says that there is a cause-and-effect relationship between two or more factors.

Examples Of Hypothesis

Examples of simple hypotheses:

• Students who consume breakfast before taking a math test will have a better overall performance than students who do not consume breakfast.
• Students who experience test anxiety before an English examination 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, is a statement that suggests that drivers who talk on the phone while driving are more likely to make mistakes.

Examples of a complex hypothesis:

• Individuals who consume a lot of sugar and don’t get much exercise are at an increased risk of developing depression.
• Younger people who are routinely exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces, according to a new study.
• Increased levels of air pollution led to higher rates of respiratory illnesses, which in turn resulted in increased costs for healthcare for the affected communities.

Examples of Directional Hypothesis:

• The crop yield will go up a lot if the amount of fertilizer is increased.
• Patients who have surgery and are exposed to more stress will need more time to get better.
• Increasing the frequency of brand advertising on social media will lead to a significant increase in brand awareness among the target audience.

Examples of Non-Directional Hypothesis (or Two-Tailed Hypothesis):

• The test scores of two groups of students are very different from each other.
• There is a link between gender and being happy at work.
• There is a correlation between the amount of caffeine an individual consumes and the speed with which they react.

Examples of a null hypothesis:

• Children who receive a new reading intervention will have scores that are different than students who do not receive the intervention.
• The results of a memory recall test will not reveal any significant gap in performance between children and adults.
• There is not a significant relationship between the number of hours spent playing video games and academic performance.

Examples of Associative Hypothesis:

• There is a link between how many hours you spend studying and how well you do in school.
• Drinking sugary drinks is bad for your health as a whole.
• There is an association between socioeconomic status and access to quality healthcare services in urban neighborhoods.

Functions Of Hypothesis

The research issue can be understood better with the help of a hypothesis, which is why developing one is crucial. The following are some of the specific roles that a hypothesis plays: (Rashid, Apr 20, 2022)

• A hypothesis gives a study a point of concentration. It enlightens us as to the specific characteristics of a study subject we need to look into.
• It instructs us on what data to acquire as well as what data we should not collect, giving the study a focal point .
• The development of a hypothesis improves objectivity since it enables the establishment of a focal point.
• A hypothesis makes it possible for us to contribute to the development of the theory. Because of this, we are in a position to definitively determine what is true and what is untrue .

How will Hypothesis help in the Scientific Method?

• The scientific method begins with observation and inquiry about the natural world when formulating research questions. Researchers can refine their observations and queries into specific, testable research questions with the aid of hypothesis. They provide an investigation with a focused starting point.
• Hypothesis generate specific predictions regarding the expected outcomes of experiments or observations. These forecasts are founded on the researcher’s current knowledge of the subject. They elucidate what researchers anticipate observing if the hypothesis is true.
• Hypothesis direct the design of experiments and data collection techniques. Researchers can use them to determine which variables to measure or manipulate, which data to obtain, and how to conduct systematic and controlled research.
• Following the formulation of a hypothesis and the design of an experiment, researchers collect data through observation, measurement, or experimentation. The collected data is used to verify the hypothesis’s predictions.
• Hypothesis establish the criteria for evaluating experiment results. The observed data are compared to the predictions generated by the hypothesis. This analysis helps determine whether empirical evidence supports or refutes the hypothesis.
• The results of experiments or observations are used to derive conclusions regarding the hypothesis. If the data support the predictions, then the hypothesis is supported. If this is not the case, the hypothesis may be revised or rejected, leading to the formulation of new queries and hypothesis.
• The scientific approach is iterative, resulting in new hypothesis and research issues from previous trials. This cycle of hypothesis generation, testing, and refining drives scientific progress.

Importance Of Hypothesis

• Hypothesis are testable statements that enable scientists to determine if their predictions are accurate. This assessment is essential to the scientific method, which is based on empirical evidence.
• Hypothesis serve as the foundation for designing experiments or data collection techniques. They can be used by researchers to develop protocols and procedures that will produce meaningful results.
• Hypothesis hold scientists accountable for their assertions. They establish expectations for what the research should reveal and enable others to assess the validity of the findings.
• Hypothesis aid in identifying the most important variables of a study. The variables can then be measured, manipulated, or analyzed to determine their relationships.
• Hypothesis assist researchers in allocating their resources efficiently. They ensure that time, money, and effort are spent investigating specific concerns, as opposed to exploring random concepts.
• Testing hypothesis contribute to the scientific body of knowledge. Whether or not a hypothesis is supported, the results contribute to our understanding of a phenomenon.
• Hypothesis can result in the creation of theories. When supported by substantive evidence, hypothesis can serve as the foundation for larger theoretical frameworks that explain complex phenomena.
• Beyond scientific research, hypothesis play a role in the solution of problems in a variety of domains. They enable professionals to make educated assumptions about the causes of problems and to devise solutions.

Research Hypotheses: Did you know that a hypothesis refers to an educated guess or prediction about the outcome of a research study?

It’s like a roadmap guiding researchers towards their destination of knowledge. Just like a compass points north, a well-crafted hypothesis points the way to valuable discoveries in the world of science and inquiry.

Choose the best answer. 

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Further reading.

• RNA-DNA World Hypothesis
• BYJU’S. (2023). Hypothesis. Retrieved 01 Septermber 2023, from https://byjus.com/physics/hypothesis/#sources-of-hypothesis
• Collegedunia. (2023). Hypothesis. Retrieved 1 September 2023, from https://collegedunia.com/exams/hypothesis-science-articleid-7026#d
• Hussain, D. J. (2022). Hypothesis. Retrieved 01 September 2023, from https://mmhapu.ac.in/doc/eContent/Management/JamesHusain/Research%20Hypothesis%20-Meaning,%20Nature%20&%20Importance-Characteristics%20of%20Good%20%20Hypothesis%20Sem2.pdf
• Media, D. (2023). Hypothesis in the Scientific Method. Retrieved 01 September 2023, from https://www.verywellmind.com/what-is-a-hypothesis-2795239#toc-hypotheses-examples
• Rashid, M. H. A. (Apr 20, 2022). Research Methodology. Retrieved 01 September 2023, from https://limbd.org/hypothesis-definitions-functions-characteristics-types-errors-the-process-of-testing-a-hypothesis-hypotheses-in-qualitative-research/#:~:text=Functions%20of%20a%20Hypothesis%3A&text=Specifically%2C%20a%20hypothesis%20serves%20the,providing%20focus%20to%20the%20study.

©BiologyOnline.com. Content provided and moderated by Biology Online Editors.

Last updated on September 8th, 2023

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scientific hypothesis

Our editors will review what you’ve submitted and determine whether to revise the article.

• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

8 Different Types of Hypotheses (Plus Essential Facts)

The hypothesis is an idea or a premise used as a jumping off the ground for further investigation. It’s essential to scientific research because it serves as a compass for scientists or researchers in carrying out their experiments or studies.

There are different types of hypotheses but crafting a good hypothesis can be tricky. A sound hypothesis should be logical, affirmative, clear, precise, quantifiable, or can be tested, and has a cause and effect factor.

Types 

Alternative hypothesis.

Also known as a maintained hypothesis or a research hypothesis, an alternative hypothesis is the exact opposite of a null hypothesis, and it is often used in statistical hypothesis testing. There are four main types of alternative hypothesis:

• Point alternative hypothesis . This hypothesis occurs when the population distribution in the hypothesis test is fully defined and has no unknown parameters. It usually has no practical interest, but it is considered important in other statistical activities.
• Non-directional alternative hypothesis. These hypotheses have nothing to do with the either region of rejection (i.e., one-tailed or two-tailed directional hypotheses) but instead, only that the null hypothesis is untrue.
• One-tailed directional hypothesis. This hypothesis is only concerned with the region of direction for one tail of a sampling distribution, not both of them.
• Two-tailed directional hypothesis. This hypothesis is concerned with both regions of rejection of a particular sampling distribution

Known by the symbol H1, this type of hypothesis proclaims the expected relationship between the variables in the theory.

Associative and Causal Hypothesis

Associative hypotheses simply state that there is a relationship between two variables, whereas causal hypotheses state that any difference in the type or amount of one particular variable is going to directly affect the difference in the type or amount of the next variable in the equation.

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These hypotheses are often used in the field of psychology. A causal hypothesis looks at how manipulation affects events in the future, while an associative hypothesis looks at how specific events co-occur.

A good example of its practical use occurs when discussing the psychological aspects of eyewitness testimonies, and they generally affect four areas of this phenomenon: emotion and memory, system variables in the line-up, estimation of the duration of the event, and own-race bias.

Complex Hypothesis

In a complex hypothesis, a relationship exists between the variables . In these hypotheses, there are more than two independent and dependent variables, as demonstrated in the following hypotheses:

• Taking drugs and smoking cigarettes leads to respiratory problems, increased tension, and cancer.
• The people who are older and living in rural areas are happier than people who are younger and who live in the city or suburbs.
• If you eat a high-fat diet and a few vegetables, you are more likely to suffer from hypertension and high cholesterol than someone who eats a lot of vegetables and sticks to a low-fat diet.

Directional Hypothesis

A directional hypothesis is one regarding either a positive or negative difference or change in the two variables involved. Typically based on aspects such as accepted theory, literature printed on the topic at hand, past research, and even accepted theory, researchers normally develop this type of hypothesis from research questions, and they use statistical methods to check its validity.

Words you often hear in hypotheses that are directional in nature include more, less, increase, decrease, positive, negative, higher, and lower. Directional hypotheses specify the direction or nature of the relationship between two or more independent variables and two or more dependent variables.

Non-Directional Hypothesis

This hypothesis states that there is a distinct relationship between two variables; however, it does not predict the exact nature or direction of that particular relationship.

Null Hypothesis

Indicated by the symbol Ho, a null hypothesis predicts that the variables in a certain hypothesis have no relationship to one another and that the hypothesis is normally subjected to some type of statistical analysis. It essentially states that the data and variables being investigated do not actually exist.

A perfect example of this comes when looking at scientific medical studies, where you have both an experimental and control group, and you are hypothesizing that there will be no difference in the results of these two groups.

Simple Hypothesis

This hypothesis consists of two variables, an independent variable or cause, and a dependent variable or cause. Simple hypotheses contain a relationship between these two variables. For example, the following are examples of simple hypotheses:

• The more you chew tobacco, the more likely you are to develop mouth cancer.
• The more money you make, the less likely you are to be involved in criminal activity.
• The more educated you are, the more likely you are to have a well-paying job.

Statistical Hypothesis

This is just a hypothesis that is able to be verified through statistics. It can be either logical or illogical, but if you can use statistics to verify it, it is called a statistical hypothesis.

Facts about Hypotheses

Difference Between Simple and Complex Hypotheses

In a simple hypothesis, there is a dependent and an independent variable, as well as a relationship between the two. The independent variable is the cause and comes first when they’re in chronological order, and the dependent variable describes the effect. In a complex hypothesis, the relationship is between two or more independent variables and two or more dependent variables.

Difference Between Non-Directional and Directional Hypotheses

In a directional research hypothesis, the direction of the relationship is predicted. The advantages of this type of hypothesis include one-tailed statistical tests, theoretical propositions that can be tested in a more precise manner, and the fact that the researcher’s expectations are very clear right from the start.

In a non-directional research hypothesis, the relationship between the variables is predicted but not the direction of that relationship. Reasons to use this type of research hypothesis include when your previous research findings contradict one another and when there is no theory on which to base your predictions.

Difference Between a Hypothesis and a Theory

There are many different differences between a theory and a hypothesis, including the following:

• A hypothesis is a suggestion of what might happen when you test out a theory. It is a prediction of a possible correlation between various phenomena. On the other hand, a theory has been tested and is well-substantiated. If a hypothesis succeeds in proving a certain point, it can then be called a theory.
• The data for a hypothesis is most often very limited, whereas the data relating to theory has been tested under numerous circumstances.
• A hypothesis offers a very specific instance; that is, it is limited to just one observation. On the other hand, a theory is more generalized and is put through a multitude of experiments and tests, which can then apply to various specific instances.
• The purposes of these two items are different as well. A hypothesis starts with a possibility that is uncertain but can be studied further via observations and experiments. A theory is used to explain why large sets of observations are continuously made.
• Hypotheses are based on various suggestions and possibilities but have uncertain results, while theories have a steady and reliable consensus among scientists and other professionals.
• Both theories and hypotheses are testable and falsifiable, but unlike theories, hypotheses are neither well-tested nor well-substantiated.

What is the Interaction Effect?

This effect describes the two variables’ relationship to one another.

When Writing the Hypothesis, There is a Certain Format to Follow

This includes three aspects:

• The correlational statement
• The comparative statement
• A statistical analysis

How are Hypotheses Used to Test Theories?

• Do not test the entire theory, just the proposition
• It can never be either proved or disproved

When Formulating a Hypothesis, There are Things to Consider

These include:

• You have to write it in the present tense
• It has to be empirically testable
• You have to write it in a declarative sentence
• It has to contain all of the variables
• It must contain three parts: the purpose statement, the problem statement, and the research question
• It has to contain the population

What is the Best Definition of a Scientific Hypothesis?

It is essentially an educated guess; however, that guess will lose its credibility if it is falsifiable.

How to Use Research Questions

There are two ways to include research questions when testing a theory. The first is in addition to a hypothesis related to the topic’s other areas of interest, and the second is in place of the actual hypothesis, which occurs in some instances.

Tips to Keep in Mind When Developing a Hypothesis

• Use language that is very precise. Your language should be concise, simple, and clean. This is not a time when you want to be vague, because everything needs to be spelled out in great detail.
• Be as logical as possible. If you believe in something, you want to prove it, and remaining logical at all times is a great start.
• Use research and experimentation to determine whether your hypothesis is testable. All hypotheses need to be proven. You have to know that proving your theory is going to work, even if you find out different in the end.

What is the Number-One Purpose of a Scientific Method?

Scientific methods are there to provide a structured way to get the appropriate evidence in order to either refute or prove a scientific hypothesis.

Glossary of Terms Related to Hypotheses

Bivariate Data: This is data that includes two distinct variables, which are random and usually graphed via a scatter plot.

Categorical Data: These data fit into a tiny number of very discrete categories. They are usually either nominal, or non-ordered, which can include things such as age or country; or they can be ordinal, or ordered, which includes aspects such as hot or cold temperature.

Correlation: This is a measure of how closely two variables are to one another. It measures whether a change in one random variable corresponds to a change in the other random variable. For example, the correlation between smoking and getting lung cancer has been widely studied.

Data: These are the results found from conducting a survey or experiment, or even an observation study of some type.

Dependent Event: If the happening of one event affects the probability of another event occurring also, they are said to be dependent events.

Distribution: The way the probability of a random variable taking a certain value is described is called its distribution. Possible distribution functions include the cumulative, probability density, or probability mass function.

Element: This refers to an object in a certain set, and that object is an element of that set.

Empirical Probability: This refers to the likelihood of an outcome happening, and it is determined by the repeat performance of a particular experiment.  You can do this by dividing the number of times that event took place by the number of times you conducted the experiment.

Equality of Sets: If two sets contain the exact same elements, they are considered equal sets. In order to determine if this is so, it can be advantageous to show that each set is contained in the other set.

Equally Likely Outcomes: Refers to outcomes that have the same probability; for example, if you toss a coin there are only two likely outcomes.

Event: This term refers to the subset of a sample space.

Expected Value: This demonstrates the average value of a quantity that is random and which has been observed numerous times in order to duplicate the same results of previous experiments.

Experiment: A scientific process that results in a set of outcomes that is observable. Even selecting a toy from a box of toys can be considered an experiment in this instance.

Experimental Probability: When you estimate how likely something is to occur, this is an experimental probability example. To get this probability, you divide the number of trials that were successful by the total number of trials that were performed.

Finite Sample Space: These sample spaces have a finite number of outcomes that could possibly occur.

Frequency: The frequency is the number of times a certain value occurs when you observe an experiment’s results.

Frequency Distribution: This refers to the data that describes possible groups or values and the frequencies that correspond to those groups or values.

Histogram: A histogram, or frequency histogram, is a bar graph that demonstrates how frequently data points occur.

Independent Event: If two events occur, and one event’s outcome has no effect on the other’s outcome, this is known as an independent event.

Infinite Sample Space: This refers to a sample space that consists of outcomes with an infinite number of possibilities.

Mutually Exclusive: Events are mutually exclusive if their outcomes have absolutely nothing in common.

Notations: Notations are operations or quantities described by symbols instead of numbers.

Observational Study: Like the name implies, these are studies that allow you to collect data through basic observation.

Odds: This is a way to express the likelihood that a certain event will happen. If you see odds of m:n, it means it is expected that a certain event will happen m times for every n times it does not happen.

One-Variable Data: Data that have related behaviors usually associated in some important way.

Outcome: The outcome is simply the result of a particular experiment. If you consider a set of all of the possible outcomes, this is called the sample space.

Probability: A probability is merely the likelihood that a certain event will take place, and it is expressed on a scale of 0 to one, with 0 meaning it is impossible that it will happen and one being a certainty that it will happen. Probability can also be expressed as a percentage, starting with 0 and ending at 100%.

Random Experiment: A random experiment is one whereby the outcome can’t be predicted with any amount of certainty, at least not before the experiment actually takes place.

Random Variable: Random variables take on different numerical values, based on the results of a particular experiment.

Replacement: Replacement is the act of returning or replacing an item back into a sample space, which takes place after an event and allows the item to be chosen more than one time.

Sample Space: This term refers to all of the possible outcomes that could result from a probability experiment.

Set: A collection of objects that is well-defined is called a set.

Simple Event: When an event is a single element of the sample space, it is known as a simple event.

Simulation: A simulation is a type of experiment that mimics a real-life event.

Single-Variable Data: These are data that use only one unknown variable.

Statistics: This is the branch of mathematics that deals with the study of quantitative data. If you analyze certain events that are governed by probability, this is called statistics.

Theoretical Probability: This probability describes the ratio of the number of outcomes in a specific event to the number of outcomes found in the sample space. It is based on the presumption that all outcomes are equally liable.

Union: Usually described by the symbol ∪, or the cup symbol, a union describes the combination of two or more sets and their elements.

Variable: A variable is a quantity that varies and is almost always represented by letters.

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Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis 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 are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. 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 which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

How to write a research hypothesis

Last updated

19 January 2023

Reviewed by

Miroslav Damyanov

Start with a broad subject matter that excites you, so your curiosity will motivate your work. Conduct a literature search to determine the range of questions already addressed and spot any holes in the existing research.

Narrow the topics that interest you and determine your research question. Rather than focusing on a hole in the research, you might choose to challenge an existing assumption, a process called problematization. You may also find yourself with a short list of questions or related topics.

Use the FINER method to determine the single problem you'll address with your research. FINER stands for:

I nteresting

You need a feasible research question, meaning that there is a way to address the question. You should find it interesting, but so should a larger audience. Rather than repeating research that others have already conducted, your research hypothesis should test something novel or unique. 

The research must fall into accepted ethical parameters as defined by the government of your country and your university or college if you're an academic. You'll also need to come up with a relevant question since your research should provide a contribution to the existing research area.

This process typically narrows your shortlist down to a single problem you'd like to study and the variable you want to test. You're ready to write your hypothesis statements.

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• Types of research hypotheses

It is important to narrow your topic down to one idea before trying to write your research hypothesis. You'll only test one problem at a time. To do this, you'll write two hypotheses – a null hypothesis (H0) and an alternative hypothesis (Ha).

You'll come across many terms related to developing a research hypothesis or referring to a specific type of hypothesis. Let's take a quick look at these terms.

Null hypothesis

The term null hypothesis refers to a research hypothesis type that assumes no statistically significant relationship exists within a set of observations or data. It represents a claim that assumes that any observed relationship is due to chance. Represented as H0, the null represents the conjecture of the research.

Alternative hypothesis

The alternative hypothesis accompanies the null hypothesis. It states that the situation presented in the null hypothesis is false or untrue, and claims an observed effect in your test. This is typically denoted by Ha or H(n), where “n” stands for the number of alternative hypotheses. You can have more than one alternative hypothesis. 

Simple hypothesis

The term simple hypothesis refers to a hypothesis or theory that predicts the relationship between two variables - the independent (predictor) and the dependent (predicted). 

Complex hypothesis

The term complex hypothesis refers to a model – either quantitative (mathematical) or qualitative . A complex hypothesis states the surmised relationship between two or more potentially related variables.

Directional hypothesis

When creating a statistical hypothesis, the directional hypothesis (the null hypothesis) states an assumption regarding one parameter of a population. Some academics call this the “one-sided” hypothesis. The alternative hypothesis indicates whether the researcher tests for a positive or negative effect by including either the greater than (">") or less than ("<") sign.

Non-directional hypothesis

We refer to the alternative hypothesis in a statistical research question as a non-directional hypothesis. It includes the not equal ("≠") sign to show that the research tests whether or not an effect exists without specifying the effect's direction (positive or negative).

Associative hypothesis

The term associative hypothesis assumes a link between two variables but stops short of stating that one variable impacts the other. Academic statistical literature asserts in this sense that correlation does not imply causation. So, although the hypothesis notes the correlation between two variables – the independent and dependent - it does not predict how the two interact.

Logical hypothesis

Typically used in philosophy rather than science, researchers can't test a logical hypothesis because the technology or data set doesn't yet exist. A logical hypothesis uses logic as the basis of its assumptions. 

In some cases, a logical hypothesis can become an empirical hypothesis once technology provides an opportunity for testing. Until that time, the question remains too expensive or complex to address. Note that a logical hypothesis is not a statistical hypothesis.

Empirical hypothesis

When we consider the opposite of a logical hypothesis, we call this an empirical or working hypothesis. This type of hypothesis considers a scientifically measurable question. A researcher can consider and test an empirical hypothesis through replicable tests, observations, and measurements.

Statistical hypothesis

The term statistical hypothesis refers to a test of a theory that uses representative statistical models to test relationships between variables to draw conclusions regarding a large population. This requires an existing large data set, commonly referred to as big data, or implementing a survey to obtain original statistical information to form a data set for the study. 

Testing this type of hypothesis requires the use of random samples. Note that the null and alternative hypotheses are used in statistical hypothesis testing.

Causal hypothesis

The term causal hypothesis refers to a research hypothesis that tests a cause-and-effect relationship. A causal hypothesis is utilized when conducting experimental or quasi-experimental research.

Descriptive hypothesis

The term descriptive hypothesis refers to a research hypothesis used in non-experimental research, specifying an influence in the relationship between two variables.

• What makes an effective research hypothesis?

An effective research hypothesis offers a clearly defined, specific statement, using simple wording that contains no assumptions or generalizations, and that you can test. A well-written hypothesis should predict the tested relationship and its outcome. It contains zero ambiguity and offers results you can observe and test. 

The research hypothesis should address a question relevant to a research area. Overall, your research hypothesis needs the following essentials:

Hypothesis Essential #1: Specificity & Clarity

Hypothesis Essential #2: Testability (Provability)

• How to develop a good research hypothesis

In developing your hypothesis statements, you must pre-plan some of your statistical analysis. Once you decide on your problem to examine, determine three aspects:

the parameter you'll test

the test's direction (left-tailed, right-tailed, or non-directional)

the hypothesized parameter value

Any quantitative research includes a hypothesized parameter value of a mean, a proportion, or the difference between two proportions. Here's how to note each parameter:

Single mean (μ)

Paired means (μd)

Single proportion (p)

Difference between two independent means (μ1−μ2)

Difference between two proportions (p1−p2)

Simple linear regression slope (β)

Correlation (ρ)

Defining these parameters and determining whether you want to test the mean, proportion, or differences helps you determine the statistical tests you'll conduct to analyze your data. When writing your hypothesis, you only need to decide which parameter to test and in what overarching way.

The null research hypothesis must include everyday language, in a single sentence, stating the problem you want to solve. Write it as an if-then statement with defined variables. Write an alternative research hypothesis that states the opposite.

• What is the correct format for writing a hypothesis?

The following example shows the proper format and textual content of a hypothesis. It follows commonly accepted academic standards.

Null hypothesis (H0): High school students who participate in varsity sports as opposed to those who do not, fail to score higher on leadership tests than students who do not participate.

Alternative hypothesis (H1): High school students who play a varsity sport as opposed to those who do not participate in team athletics will score higher on leadership tests than students who do not participate in athletics.

The research question tests the correlation between varsity sports participation and leadership qualities expressed as a score on leadership tests. It compares the population of athletes to non-athletes.

• What are the five steps of a hypothesis?

Once you decide on the specific problem or question you want to address, you can write your research hypothesis. Use this five-step system to hone your null hypothesis and generate your alternative hypothesis.

Step 1 : Create your research question. This topic should interest and excite you; answering it provides relevant information to an industry or academic area.

Step 2 : Conduct a literature review to gather essential existing research.

Step 3 : Write a clear, strong, simply worded sentence that explains your test parameter, test direction, and hypothesized parameter.

Step 4 : Read it a few times. Have others read it and ask them what they think it means. Refine your statement accordingly until it becomes understandable to everyone. While not everyone can or will comprehend every research study conducted, any person from the general population should be able to read your hypothesis and alternative hypothesis and understand the essential question you want to answer.

Step 5 : Re-write your null hypothesis until it reads simply and understandably. Write your alternative hypothesis.

What is the Red Queen hypothesis?

Some hypotheses are well-known, such as the Red Queen hypothesis. Choose your wording carefully, since you could become like the famed scientist Dr. Leigh Van Valen. In 1973, Dr. Van Valen proposed the Red Queen hypothesis to describe coevolutionary activity, specifically reciprocal evolutionary effects between species to explain extinction rates in the fossil record. 

Essentially, Van Valen theorized that to survive, each species remains in a constant state of adaptation, evolution, and proliferation, and constantly competes for survival alongside other species doing the same. Only by doing this can a species avoid extinction. Van Valen took the hypothesis title from the Lewis Carroll book, "Through the Looking Glass," which contains a key character named the Red Queen who explains to Alice that for all of her running, she's merely running in place.

• Getting started with your research

In conclusion, once you write your null hypothesis (H0) and an alternative hypothesis (Ha), you’ve essentially authored the elevator pitch of your research. These two one-sentence statements describe your topic in simple, understandable terms that both professionals and laymen can understand. They provide the starting point of your research project.

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Definition of a Hypothesis

What it is and how it's used in sociology

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A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

Null Hypothesis

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Alternative Hypothesis

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Updated by Nicki Lisa Cole, Ph.D

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The Impact of Complexity on Methods and Findings in Psychological Science

The study of human behavior is severely hampered by logistical problems, ethical and legal constraints, and funding shortfalls. However, the biggest difficulty of conducting social and behavioral research is the extraordinary complexity of the study phenomena. In this article, we review the impact of complexity on research design, hypothesis testing, measurement, data analyses, reproducibility, and the communication of findings in psychological science. The systematic investigation of the world often requires different approaches because of the variability in complexity. Confirmatory testing, multi-factorial designs, survey methods, large samples, and modeling are frequently needed to study complex social and behavioral topics. Complexity impedes the measurement of general constructs, the reproducibility of results and scientific reporting, and the general rigor of research. Many of the benchmarks established by classic work in physical science are not attainable in studies of more complex phenomena. Consequently, the standards used to evaluate scientific research should be tethered to the complexity of the study topic.

Introduction

For decades, researchers in the physical sciences have implored scientists in other fields to be like them. The assumption underlying this advice has been that disciplines such as economics and geology would advance more rapidly if they adopted the practices and standards of fields such as chemistry and astronomy. While much of this patronizing guidance has ebbed, the thinking still persists. In 2005, the journal Nature published an editorial entitled “ In praise of soft science ” featuring the lead: “‘Hard’ scientists should stop looking down their noses at social scientists, and instead share methods that could help them address pressing societal problems” (p. 1003). The bottom line of the condescending editorial was that social scientists are studying important topics; they just need help from the real sciences to progress.

The attitude of many researchers in the “hard” sciences is understandable, given the vast differences in the achievements of fields. The truth is that the social and behavioral sciences have made substantially less progress than other disciplines in developing precise quantitative theories and efficacious interventions and treatments. The contrasts in success and stature have motivated generations of researchers to strive to be like physicists and chemists. In fact, for over half a century, many social and behavioral scientists dutifully adopted the methods and approaches of the physical sciences.

Scholars have long believed that as the social and behavioral sciences matured, they would become more like their highly regarded counterparts. The assumption here has been that with the passage of time, the research procedures, designs, and measures in disciplines such as psychology and sociology would become more rigorous and refined. This, in turn, would lead to the establishment of precise theories and laws on par with those of physics and astronomy. However, as greater effort and resources have been invested in the study of behavior and groups, research methodologies have tended to become more varied and diverse. If anything, the social and behavioral sciences have become increasingly dissimilar from the “hard” sciences as they have matured.

Science Is Not Always the Same

Scientists across disciplines share the same basic scholarly aspirations; they seek to describe and explain the world, to predict important events, and to develop applications that benefit their communities. While the general goals of all scientists are largely the same, the specific manner in which research is conducted varies substantially from discipline to discipline. This variability is most apparent in comparisons between the social and behavioral sciences, whose practices have been labeled “soft,” and the natural sciences, whose practices have been labeled “hard” (e.g., Storer, 1967 ).

There are many reasons why science is often conducted differently by social and behavior researchers. The procedures, measures, and treatments used in the social and behavioral studies are severely restricted by the legal and institutional regulations, moral concerns, and ethical guidelines governing human research. In addition, the scope, depth, and technology of studies have been limited by the historically low levels of funding allocated to the social and behavioral sciences. Research is further hampered by the tendency for humans to behave differently when they perceive that they are being observed and studied (e.g., Orne, 1962 ; Levitt and List, 2011 ). However, we believe that the primary contributor to disciplinary differences in the scientific enterprise is the variability in the complexity of the topics that are studied. Social and behavioral scientists generally investigate more complex phenomena than natural scientists. From decades of research, they have learned that traditional scientific approaches are not always well suited for the examination of challenging topics, such as the dynamics of organizations and the machinations of the mind. Researchers in fields, such as psychology, anthropology, and sociology, have found it necessary to develop new methods and approaches to study phenomena that are lacking in regularity and predictability.

Prior research (e.g., Cole, 1983 ; Simonton, 2004 ; Fanelli and Glänzel, 2013 ) has uncovered many important differences in research practices between disciplines in the context of investigating Comte’s (1855) hierarchy of sciences. Comte postulated that the sciences could be ordered in a hierarchy of increasing complexity and dependency and decreasing generality beginning with astronomy followed by physics, chemistry, biology, and sociology. Simonton (2004 , 2015 ), Fanelli and Glänzel (2013) , and other scholars have drawn from numerous sources, including publishing trends, surveys, and citations, to show that fields vary in generality, dependency, and complexity in a manner consistent with the rankings postulated by Comte.

While the research on Comte’s (1855) hierarchy has revealed many important disciplinary differences in scientific practice, we believe that the complexity of a research topic has much greater impact than has been previously recognized. In our view, complexity affects almost every facet of the scientific enterprise from research design to measurement to statistical analysis to scientific reporting. In this paper, we examine how the practice of science in psychology has been shaped by the complexity of the study phenomena. While the focus is psychology and other social and behavioral fields, we believe that our observations hold true for studies of complex phenomena in other scientific disciplines.

Our paper does not review the enormous literature on complexity in science or present new insights about the workings of complex systems. The paper also does not present a comprehensive review of all of the ways in which psychological research deviates from more traditional methods. Rather, we focus on the most central practices that are affected by the complexity of the study phenomena. In particular, we examine the impact of complexity on the design of studies, testing of hypotheses, measurement of variables, reporting of results, reproduction of findings, and general rigor in psychology.

The Impact of Complexity on Theory Development

There is no consensus for a singular definition of “complexity” in science ( Zuchowski, 2012 ; Fanelli and Glänzel, 2013 ). For purposes of this paper, complexity is defined by emergent properties, processes, and the behavior that are not reducible to lower level mechanisms or the workings of the individual parts (e.g., Van Regenmortel, 2004 ; Mazzocchi, 2008 ). Complexity is further defined by the number of components of a system, and the number and non-linearity of the connections or interrelations between the components. While emergent phenomena are commonly stable and measurable (e.g., Bedau and Humphreys, 2008 ; Taylor, 2009 ), complex systems are often mutable and change as a function of the interactions between their components and encounters with the broader environment. Thus, they have hysteresis, that is, their current state is dependent on their history.

Developing precise theories of the countless components and relations that often characterize complex systems is a formidable task. Sanbonmatsu and Johnston (2019) suggest that, as the phenomena of study become more complex, the development of theory is impeded by an increasing tradeoff between generality and precision. Scientists attempt to develop general theories that are comprehensive yet parsimonious. Limiting the number of terms or statements of a theory is important, of course, because it facilitates explanation, understanding, and communication. However, when the study topic is complex, the causes and moderators of important effects are more numerous and much less invariant. To represent this variability accurately, the descriptions of the causal relations and definitions of broad constructs have to be abstract and imprecise. Because of this, many theories in psychology and other social and behavioral sciences are only vaguely true. They are often presented in such qualitative terms that they are almost impossible to falsify.

While general theories may explain complex phenomena, they commonly lack the specificity to make exacting predictions. To increase precision, scientists have to focus on proximal determinants and develop applied theories and models that are specific to particular contexts and entities. Although these theories and models facilitate the development of applications and interventions, they tend to lack any semblance of generality.

Understanding the impact of complexity on theory development is important to our examination of disciplinary differences in scientific practice because theory drives research. Theory determines to a large degree the hypotheses that are tested, the constructs that are measured, and the general rigor of research. Complexity often influences methods by shaping the specificity of the theories and hypotheses that guide research. As we discuss, many of the challenges and problems characterizing research in psychological science and other disciplines are rooted in the difficulties of conceptualizing complex phenomena.

Moving Target

The phenomena of interest in physics are truly general. The phenomena that sociologists attempt to understand frequently change faster than we are able to adequately describe them…. In sociology, by the time any theory is developed that might explain a particular phenomenon, it is possible that the phenomenon and the factors causing it will have changed. In short, sociologists are shooting at a moving target – a target that frequently has changed or disappeared by the time the bullet arrives ( Cole, 1994 , p. 138–139).

Basic researchers regard science as the search for eternal truths about the universe. However, there are obvious contrasts between disciplines and fields in the invariance of the causal relations that are studied and reported. One of the most fundamental differences between the biological, social, and behavioral sciences and the physical sciences is the “mutability” of the study phenomena. As Cole (1994) suggests in the quote above, researchers in complex fields are much more likely to study moving targets; that is, they are much more likely to investigate processes, behaviors, properties, and entities that change over time.

There are many mechanisms through which traits and behaviors “mutate.” Most fundamentally, alterations in the DNA sequence of genes may arise as a result of errors in the replication process. Environmental pressures, aging, or changes in health can also lead to epigenetic variations in the expression of genes that shape the behavior of organisms.

Causal relations may be altered through more social mechanisms. Changes in the structures, ideologies, laws, and norms of societies can dramatically alter peoples’ beliefs and actions. Broader changes in ecosystems, physical environments, and technological developments similarly impact how societies and individuals function. The patterns of responding also change through experience and learning. Some of these alterations are fostered by research. Unlike physical phenomena, human activity is directly impacted by the communication of scientific findings. As Gergen (1973 , p. 313) stated,

“Herein lies a fundamental difference between the natural and social sciences. In the former, the scientist cannot typically communicate his knowledge to the subjects of his study such that their behavioral dispositions are modified. In the social sciences such communications can have a vital impact on behavior.”

The tendency for individual and group behavior to “mutate” adds another level of complexity to social and behavior science. Not only are the targets of research difficult to pinpoint, they are constantly moving. Moreover, just as scientists begin to track the target, their own research sometimes causes it to change direction.

Confirmatory Hypothesis Testing in Psychological Science

Popper (1959) believed that science progresses primarily through falsification. He argued that, while theories can never be conclusively verified, they can be dismissed by a single disconfirming observation. In his view, the best scientific theories and hypotheses are falsifiable. In a related vein, Platt (1964) argued that, for “strong inference,” experiments need to eliminate less viable hypotheses in crucial tests. However, many philosophers and scientists have argued that scientific theories are based more on corroborations than falsifications (e.g., Ladyman, 2002 ). Disconfirmations are commonly dismissed because of misassumptions in the “auxiliary hypotheses” ( Quine, 1953 ; Duhem, 1962 ). Moreover, theories are typically adjusted to accommodate disconfirming findings ( Lakatos, 1978 ). Similarly, crucial tests often fall short because hypotheses are conditional and modifiable ( O’Donohue and Buchanan, 2001 ; Davis, 2006 ).

Some researchers (e.g., Meehl, 1978 ) have argued that psychological science needs to utilize more of the falsification strategy prescribed by Popper (1959) to progress. However, the complexity of social and behavioral phenomena often precludes the development and testing of falsifiable theories.

The Diagnosticity of Confirmatory vs. Disconfirmatory Evidence Depends on the Test Hypothesis

Sanbonmatsu et al. (2005) argued that the appropriateness of a confirmatory vs. disconfirmatory strategy depends on the type of hypothesis under investigation (for purposes of this paper, “confirmatory” and “disconfirmatory” strategies are equated with “positive” and “negative” searches, respectively; see Klayman and Ha, 1987 ). Hypotheses specify the proportion of instances that are characterized by a particular relation or effect. At the broadest level, hypotheses are either absolute in presuming that a particular relation is always present or absent or non-absolute in presuming that a relation is sometimes present or absent. The diagnosticity of evidence depends on the hypothesized frequency of the test relation. Diagnosticity can be defined in terms of the degree to which the information distinguishes the test hypothesis from its complement (e.g., Fischhoff and Beyth-Marom, 1983 ). In tests of absolute or universal hypotheses, disconfirmations have considerably greater diagnostic value than confirmations. A confirming observation is possible not only when an absolute hypothesis is true but also when it is false. However, a disconfirming instance is not possible when a universal hypothesis is true. This, of course, is in keeping with Popper’s (1959) analysis of the utility of falsification in science. In contrast, confirmations are much more diagnostic than disconfirmations in tests of the non-absolute hypothesis that an effect occurs sometimes or in some conditions. While a confirming observation is possible only when a non-absolute hypothesis is true, a disconfirming observation is possible when a non-absolute hypothesis is both true and false.

People are generally cognizant that the diagnosticity of confirming vs. disconfirming evidence depends on the universality of the hypothesized relation. Research has shown that both laypersons ( Sanbonmatsu et al., 2005 ) and psychological scientists ( Sanbonmatsu et al., 2015 ) are more likely to use a confirmatory approach in the testing of non-absolute hypotheses and a disconfirmatory approach in the testing of universal hypotheses.

Social and Behavioral Studies Are More Likely to Take a Confirmatory Approach Because of Complexity

The diagnosticity of disconfirmatory vs. confirmatory evidence varies as a function of the complexity of the study phenomena, and, hence, scientific discipline. When the subject matter is complex, there are more numerous causes, interactions, emergent processes, and non-linear relations. Because of this, the hypothesized relations that are tested are typically far from universal. Consequently, confirmations of hypotheses about complex phenomena are generally much more informative than disconfirmations. In contrast, disconfirmations are often more informative than confirmations in studies of simple phenomena because the hypothesis that are tested are often universal or near universal.

Theories in the natural sciences tend to be more falsifiable and, hence, “better” than theories in the social and behavioral sciences because the study phenomena are generally simpler and more invariant. The hypotheses that are generated in fields such as molecular biology are more likely to be absolute or near absolute and more subject to falsification. Thus, while confirmatory testing predominates all fields, disconfirmatory studies and “crucial tests” ( Platt, 1964 ) may be conducted more frequently in the natural sciences than in the social and behavioral sciences.

Because of the complexity of social and behavioral phenomena, psychologists tend to use a confirmatory approach in their studies ( Uchino et al., 2010 ). Evidence suggests that they are more apt to seek confirmation because the hypotheses they generate and test are generally non-universal ( Sanbonmatsu et al., 2015 ). Disconfirmations are also less meaningful in the social and behavioral sciences because of the widespread methodological difficulties surrounding the study of complex phenomena. Research studies that fail to support the test hypothesis are frequently dismissed because of the shortcomings in the methods. As we will discuss later, studies in the social and behavioral sciences are commonly saddled with imprecise measures, weak manipulations, and a general lack of rigor because of the complexity of the study phenomena. These methodological issues undermine the theoretical conclusions that can be drawn from disconfirmatory findings, and hence, the extent to which they are publishable. We speculate that there are stronger norms against publishing negative results in fields such as psychology precisely because disconfirmations are generally less informative.

Evidence that the publishing of positive findings is more common in the social and behavioral sciences than in other fields was provided in an archival study by Fanelli (2010) . The study analyzed over two thousand papers from numerous disciplines in which the testing of a hypothesis was reported. Positive results were reported with much greater frequency in social and behavioral science papers (including business) than in physical science and space science articles. While there are undoubtedly a number of contributing factors, we believe that the differences in the reporting of positive results across disciplines stem primarily from the lower informativeness of negative findings in fields such as psychology and sociology.

Social and behavioral scientists have been criticized for decades because of the lack of falsifiability of their theories (e.g., Popper, 1959 ). However, it seems unlikely that social and behavioral theories and hypotheses are less universal and, hence, less falsifiable because researchers are incapable of discovering patterns that are more uniform and general. It is equally incredulous that researchers would deliberately opt for more nuanced and convoluted theories over more parsimonious, general, and predictive conceptualizations of the world. The complex phenomena studied in the social and behavioral sciences simply do not lend themselves to the development of falsifiable theories.

Disciplinary Differences in Design, Analyses, and Reporting

One of the most significant contributors to greater methodological diversity in science is the complexity of the study phenomena. In this section of the paper, we review the impact of complexity on research design, sampling, analyses, and reporting in psychology and other social and behavioral sciences. As we discuss, the numerousness and variability of the causal relations, emergent processes, and weak and inconsistent effects characteristic of complex phenomena often necessitate non-traditional methods.

Research Design

To begin to understand complex social and behavioral phenomena, researchers often have to assess the role of multiple interactive causes and processes (e.g., Stanovich, 2019 ). Consequently, simple experiments often will not do (e.g., Cronbach, 1957 , 1975 ). We speculate that experiments in psychological science are much more apt to be multi-factorial than experiments in the natural sciences. Psychologists may also be more likely to conduct a series of interrelated experiments to test potential determinants, moderators, and underlying processes. Indeed, in some of the more prominent journals in the field, papers are not publishable unless the reported findings entail this level of scope.

One of the major difficulties of studying the complex phenomena is the numerousness of the relevant variables (e.g., Meehl, 1978 ). Often, there are too many potential causes of complex phenomena to experimentally manipulate. Consequently, observational and survey methods are frequently utilized because they enable researchers to quickly and economically measure large swaths of variables and interrelations. The numerosity of variables also makes statistical analyses more challenging and creates potential pitfalls such as the omitted variable bias in regression.

To more fully capture the nuances of complex phenomena and the natural settings in which they are grounded, social and behavioral scientists conduct qualitative studies (e.g., Henwood and Pidgeon, 1992 ). Qualitative research often better captures the individual perspectives that shape behavior and is generally more sensitive to the context in which the causal effects occur than experimental designs. Case studies have been the important starting point for theory development and hypothesis generation in clinical neuropsychology (e.g., Damasio, 1994 ).

The tremendous variability of the components of many complex systems necessitates careful sampling. This is particularly true in social and behavioral research because every person is different. While testing the properties of a particular chemical compound generally does not require a sampling plan, the selection of an appropriate cohort is crucial to the external validity of a psychology study. Unfortunately, the logistics of obtaining a sample that is representative of all of humanity are so daunting and overwhelming that the field generally resorts to convenience and rationalization. The most common justification for not obtaining a representative sample is that psychologists are studying “basic processes” that are largely the same across persons. However, when studies fail to replicate because of sampling and other “random” sources of variability (e.g., Open Science Collaboration, 2015 ; Stanley et al., 2018 ), it becomes all too apparent that the processes and effects being investigated are not “basic” and universal and that convenience samples are not justifiable on conceptual grounds.

While most discussions of sampling focus on the selection of persons, the reality is that most of the behaviors and processes that are investigated in fields such as psychology and economics are affected by other study characteristics, such as the physical environment, the immediate social situation, culture, time, and operationalizations of the independent and dependent measures ( Fiedler, 2011 ). However, because of the impossibility of constructing studies that are representative with respect to all facets of the procedures and context, researchers also resort to convenience in sampling these critical components. Studies are typically limited to one physical environment, one manipulation, one cultural setting, and one of every other important study element. Obviously, sampling is less of an issue in the physical sciences because the entities and causal relations that are investigated are often more uniform across time and place.

Statistical Analyses

Statistics utilized across science are all basically the same. However, it is common to attempt to address many of the issues inherent in the study of complex topics through varying statistical techniques. For example, measurement issues are commonly modeled through latent variables within structural equation modeling as a means to account for measurement imprecision ( Bollen, 1989 ); random effects are integral to mixed models as a way to allow for different effects from different people ( Raudenbush and Bryk, 2002 ); and integrative data analysis seeks to expand on meta-analytic principles by simultaneously analyzing multiple data sets as a way to bridge replication and sampling issues ( Curran and Hussong, 2009 ). So, it is the very issues discussed herein that frequently relate to disciplinary differences in the use of statistics. Strict experimentation with strong effects and clean measurement, for example, enables simpler statistical approaches.

Statistics play a central role in psychology when considering causal claims. The social and behavioral sciences heavily draw from Rubin’s (1974) causal model for experimentation. Random assignments, within subjects designs, and comparisons based on having identical units are all attempts to resolve causal issues. However, in the social and behavioral sciences, it is not always easy to apply Rubin’s model. For example, the model only applies to causes that can be manipulated, which cannot always be done or done ethically. Psychology’s reliance on quasi-experimentation and non-experimentation have instead relied on statistical associations in conjunction with the methodology to make up for the difference ( Berk, 1988 ). Granger’s (1988) causal arguments rely on our ability to statistically control for other possible explanations. However, under complexity, linear and even non-linear associations that are estimated statistically may fail to meet Granger’s criteria ( Sugihara et al., 2012 ), suggesting that causal arguments themselves may be difficult to claim.

Scientific Reporting

Research suggests that dissertation abstracts and texts are longer in the social and behavioral sciences than in the physical sciences ( Ashar and Shapiro, 1990 , Table 2, p. 131). Similarly, Fanelli and Glänzel (2013) showed that as research fields get “softer,” the page length of articles tends to increase. Scientific articles may be longer in more complex disciplines because there is typically more to describe and explain. For example, in social and behavioral science papers, there are often more study hypotheses to justify and more measures to describe. Moreover, because of a less standardization, the procedures often require greater detailing. Results sections commonly go on and on because there are so many analyses to report. Finally, discussions are often lengthy because of inconsistencies in the data and discrepancies with the prevailing theory.

Findings are often reported differently in the social and behavioral sciences than in less complex disciplines, such as physics and molecular biology, because of the messiness of the results. Studies of what Simonton (2004) calls “graph prominence” have shown that articles in the natural sciences are much more likely to the results of the present study in graphs than articles in the social and behavioral sciences ( Cleveland, 1984 ; Smith et al., 2000 ). Simonton (2015 , p. 340) explains:

In the physical sciences, and to a slightly lesser extent the biological sciences, the results tend to be so clean, and the effect sizes so large, that the findings can easily be depicted in visual form. The error bars around a fitted curve are small, even trivial. By comparison, psychology and especially sociology deal with phenomena so complex that the results cannot be so simply portrayed. Hence, the findings may have to be presented in statistical tables, with the number of asterisks deceptively indicating importance ( Meehl, 1978 ).

The imprecision of theories and the variability of findings in more complex fields also affects verbal presentations of research findings. Schachter et al. (1991) showed that undergraduate classroom lectures in the social and behavioral sciences and humanities were characterized by more frequent pauses (“uh,” “er,” and “um”) than similar lectures in the natural sciences. The variation in delivery was due to differences in content rather than skill as speech did not vary in disfluency when lecturers were interviewed about a common subject (teaching). Fluency is often lacking in the presentation of social and behavioral research because theories and findings are more complicated and require greater qualification. Psychologists routinely have to use phrases, such as “often,” “sometimes,” and “tends to,” that limits the generality and the strength of their statements because of the variability of the described relations and effects ( Sanbonmatsu and Johnston, 2019 ).

“Inadequate” Measurement

I should like to venture the judgment that it is inadequate measurement, more than inadequate concept or hypothesis, that has plagued social researchers and prevented fuller explanations of the variances with which they are confounded ( Hauser, 1969 , p. 129).

The measurement of theoretical constructs is challenging in every field of science. However, it is especially problematic in more complex disciplines. Without question, measurement is one of the criticized facets of social and behavioral studies. Researchers in the natural sciences commonly roll their eyes at the scales and instruments that are administered in fields such as psychology and education. Even social and behavioral researchers are often openly critical of the measures in their fields (e.g., Mitchell, 1999 ; Fried and Flake, 2018 ). We will not attempt to review the numerous shortcomings of social and behavioral measures that have been explicated in the literature. Instead, our focus is on why measurement is an intractable problem in the study of complex phenomena.

The Problem of Generality

Many researchers (e.g., Mitchell, 1999 ; Finkelstein, 2005 ) have suggested that one of the principal reasons why measures are bad in the social and behavioral sciences is sketchy concepts and theories. Theoretical constructs in fields such as psychology and sociology lack the definitiveness needed for the development of valid instruments and scales (e.g., Meehl, 1978 ).

Scientific measurement is not a simple mechanical procedure of assigning numbers or symbols to attributes or events. The process generally begins with theory; theory determines to a large degree what is measured and how it is measured (e.g., Borsboom et al., 2004 ; Bringmann and Eronen, 2016 ). Scientists attempt to develop theories that are parsimonious and that have scope. This requires the development of constructs that are general and that can represent a broad array of attributes, processes, states, entities, or events. However, when the phenomena are complex, the instances or members of a construct are often highly diverse. In order to accommodate this variability, the theoretical constructs have to be defined abstractly and vaguely. It is these qualities that allow constructs to be more inclusive and general and applicable to a broader set of instances and contexts. As Zeller and Carmines (1980 , p. 3) observed, “…abstract concepts can only be approximated by empirical indicants. Indeed, it is the very vagueness, complexity, and suggestiveness of concepts that allow them to be empirically referenced with varying degrees of success at different times and places.”

However, these qualities also contribute to seemingly never-ending debates about how to define theoretical constructs. For example, social and behavioral scientists have argued for decades about how to best define constructs such as “community,” “social class,” and “reward.”

More significantly, the lack of definitiveness of theoretical constructs of complex phenomena allows greater leeway in how they are operationalized. Because of their scope, none of the operationalizations fully capture all the facets and instances of the constructs ( Zeller and Carmines, 1980 ). Moreover, theoretical constructs are often defined so abstractly and loosely that they are inclusive of properties, processes, and entities that they are not intended to represent. Thus, theoretical constructs that are supposedly distinct are commonly overlapping and redundant with one another, which leads to seemingly never ending debates about whether and how constructs are different from one another.

Finally, general constructs are often defined so abstractly and vaguely that they cannot possibly be precisely scaled. Because many constructs amount to loose configurations of attributes, beliefs, states, behaviors, and processes that are lacking a quantitative or quantifiable structure ( Mitchell, 1997 , 1999 ), it is impossible to determine exactly what increases and decreases in the constructs mean or entail. These, of course, are the fundamental issues of validity that bedevil measurement in the social and behavioral sciences. When constructs are defined abstractly to be inclusive of a broad array of properties, it is almost impossible to develop measures that correspond to the intended concept and only the intended concept.

Many researchers are aware of the disconnect between the constructs of complex theories and the measures that are used to measure them. To increase uniformity and precision, some resort to narrowing their theories and defining their constructs in more operational terms. This may seem like an instance of putting the cart before the horse but tying theories more closely to scales and instruments is frequently necessary to deal with the measurement morass pervading the study of complex phenomena. Applied quantitative models in the social and behavioral sciences are routinely limited to variables that have well established and tightly defined operationalizations.

The problem with defining constructs more narrowly, of course, is that it reduces the scope of the theory. This is the terrible tradeoff between generality and precision that researchers commonly have to make in the development of theories of complex topics ( Sanbonmatsu and Johnston, 2019 ). Psychological scientists create basic theories to facilitate the explanation and understanding of a broad array of phenomena. However, the theories invariably lack the specificity and definition needed for precise measurement and prediction. While narrowing the constructs allows for the development of more valid and precise measures, it comes at the expense of the generality of the theory.

This tradeoff is manifested in the literature on self-esteem. The great strength of the construct is its scope and explanatory value. Studies of self-esteem help to explain, in part, a wide variety of important outcomes ranging from resistance to persuasion to occupational and educational success. However, the breadth of the construct is also its weakness. Because self-esteem is so loosely defined, there are a multitude of different scales used to measure it, many of which are weakly correlated or uncorrelated with one another ( Wells and Marwell, 1976 ; Wylie, 1979 ). Naturally, this has led to varied and sometimes conflicting findings which have limited the ability of researchers to draw coherent theoretical conclusions about the causes and effects of self-esteem. What appears clear is that, with the possible exception of happiness, self-esteem is not a major predictor of anything ( Baumeister et al., 2003 ), a pattern that is common for constructs that are broad and amorphous. Finally, there are numerous constructs, such as narcissism and extraversion, that co-vary significantly with self-esteem in the prediction of important outcomes. Not surprisingly, there are long standing disagreements about how to define and measure self-esteem and about how self-esteem differs from other personality variables.

In our view, the difficulties of measuring general constructs of complex social and behavioral phenomena are largely intractable. To accurately represent the variability characterizing broad and often diverse categories of complex entities, properties, and events, general constructs must be abstract and vague to the point that the development of highly valid and precise instruments is precluded. To develop better measures, the scope of the constructs of a theory or model must be limited.

Internal Constructs and Processes

A second way in which theory contributes to inadequate measurement in psychology is the postulation of cognitive and affective processes, states, and constructs such as traits and attitudes that are not directly observable. As generations of researchers have pointed out, these constructs do not lend themselves to precise scaling (e.g., Mitchell, 1999 ; Blanton and Jaccard, 2006 ). Moreover, they are generally measured through self-report, which is fraught with problems such as social desirability bias and misremembering. While cognitive neuroscientists are working diligently to develop psychophysiological measures of mental processes, self-report remains by far the most common and best method to measure constructs such as perceptions, intentions, feelings, and traits.

However, the complexity of social and behavioral phenomena necessitates the inclusion of internal processes in the construction of psychological theories. Researchers have long realized that, to explain and predict behavior, they often must analyze both the stimulus situation and individuals’ unique processing of and responding to the stimulus situation. The measurement of mental states and constructs enables researchers to account for the idiosyncrasies of persons in their theories.

Moreover, because mental processes are generally the most proximal determinants of responding, they are often the best predictor. Human action is often shaped by innumerable events and developments that take place over a lengthy period of time. Researchers have learned that complex behaviors are generally much better predicted by internal constructs, such as traits, attitudes, and intentions, than by more temporally distal forces. The prevailing hope in psychology, of course, is that the greatest advancements in the field will be achieved through the study of the proximal psychophysiological processes underlying cognition, affect, and action. Thus, while the postulation of internal processes and constructs in psychological theories creates a myriad of measurement issues, it is essential for the explanation and prediction of behavior.

The Effect of Complexity on Replication

Is there currently a crisis of confidence in psychological science reflecting an unprecedented level of doubt among practitioners about the reliability of research findings in the field? It would certainly appear that there is ( Pashler and Wagenmakers, 2012 , p. 528).

A presumed requirement of rigorous scientific inquiry is replication (e.g., Francis, 2012 ); research findings must be reproduced to ensure that they are reliable and, hence, a sound basis for scientific inference. Replication has been idealized as a cornerstone of science in every discipline from chemistry to psychology. However, in our view, substantial variability in the frequency and degree of reproduction of studies is to be expected across fields because of differences in the complexity of the study phenomena.

The belief in the sanctity of reproduction is based heavily on research in the physical sciences in which findings have been regularly reproduced because the study phenomena are often relatively simple. The causal relations that are investigated are commonly strong and invariant across context which makes reproduction more likely. However, as complexity increases, the connections between constructs tend to be weaker and more numerous and variable. Moreover, complex systems often mutate as a result of encounters with the broader environment. Thus, theory suggests that as the complexity of the topics increases, the uniformity of relations and effects across context and time should be lower. In particular, findings in psychology and other social and behavioral sciences should commonly be less robust and reproducible than findings in the physical sciences.

Consistent with theory, several systematic, large-scale replication projects have shown that the reproduction rates of published psychology studies are often low. For example, in the Open Science Collaboration (2015) , only 36% of the replication studies reported statistically significant results whereas 97% of the original studies reported significant results. The effect sizes were half the magnitude of the original study effects. A replication of social science studies reported in Nature and Science from 2010 to 2015 ( Camerer et al., 2018 ) generated significant findings similar to those of the original study in 62% of the replications, though the effect sizes were substantially lower. In the most recent Many Labs Reproducibility Project ( Klein et al., 2018 ), 54% of the replications found a statistically significant effect in the same direction of the original finding. Most (75%) observed effects were smaller than those of the original study. Thus, while a large proportion of the studies were reproduced by these arduous and important replication projects, a similar large proportion of the studies were not reproduced. Moreover, the effect sizes observed in the replications were generally smaller.

The impact of complexity on the reproducibility of research findings is further implicated by data showing that the replication rates are lower in social psychology than in cognitive psychology ( Open Science Collaboration, 2015 ). There are undoubtedly many factors that contribute to differences in reproducibility between these sub-disciplines, including the greater utilization of within-subjects designs and multiple trials in cognitive psychology. However, we would expect social psychological studies to be more difficult to replicate given the greater complexity of the phenomena that are studied. Researchers in the field generally investigate complex interpersonal and group processes and social behaviors and cognitions that are subject to substantial variation as a function of persons, culture, and time. The effects observed in social psychology studies are also commonly weaker ( Open Science Collaboration, 2015 ), which further diminishes the likelihood of replication.

Finally, a related analysis of the replication data generated by the Open Science Collaboration (2015) by Van Bavel et al. (2016) suggests that the contextual sensitivity of findings in psychology affects their reproducibility. Correlations and effects that were perceived to be more readily influenced by culture, time, and place were less likely to be successfully replicated. Again, complex phenomena are moderated by a broader array of components or constructs, and, hence, are less consistent across contexts.

Why Has There Not Been a Replication Crisis in Other Social and Behavioral Disciplines?

Our analysis of the effects of complexity on reproduction suggests that the difficulties in replicating research findings should be greatest in the social and behavioral disciplines that examine organizational, societal, and cultural processes because these phenomena are the most complex. While replication has become an important issue and topic of research in economics (e.g., Camerer et al., 2016 ; Berry et al., 2017 ; Clemens, 2017 ), there does not appear to have been a replication crisis in anthropology, sociology, and political science and allied fields such as management, and communication. Certainly none of these social and behavioral disciplines has endured the widespread hand wringing, finger pointing, self-loathing, and general angst that has beset psychology.

There are probably many reasons why there has not been an uproar about replication in most social and behavioral sciences. Fields such as sociology, political science, and economics make heavy use of data collected by government agencies and other organizations that are shared by and available to most investigators which serves to minimize misuse or misconduct. In addition, researchers in these fields are less likely to conduct experiments which have been the primary target of most replication efforts. However, we speculate that the main reason why the turmoil has been limited to psychological science is because research findings are less expected to replicate in other social and behavioral disciplines. Sociologists, political scientists, organizational and management scientists, and anthropologists may be more cognizant of how the behaviors and processes they investigate vary as a function of societal conditions, groups, and cultures. In addition, they may be more apt to recognize that changes in the world can radically alter the functioning of individuals, organizations, and communities. In contrast, many theoretically oriented psychologists are convinced that they are investigating “basic processes” that are largely consistent across context and enduring for all time. However, while many psychophysiological and cognitive studies examine relatively simple relations and effects that are often highly reproducible, the replication literature suggests that the topics of study in most domains of psychology are not so “basic” and invariant.

Replicating Variable and Weak Effects

The assumption that research findings must be reproducible and reproduced is another belief that has been adopted from the natural sciences where the phenomena of study are simpler. However, this convention may not apply to more complex sciences such as psychology because of the variable and weak effects that are often studied.

Numerous analyses have shown that a principal contributor to replication failure in psychological science is the combination of random error and small sample sizes (e.g., Stanley and Spence, 2014 ; Loken and Gelman, 2017 ). There appears to be consensus that the majority of studies in psychology have been severely underpowered (e.g., Cohen, 1962 ; Maxwell, 2004 ; Fraley and Vazire, 2014 ), given the sizeable noise that surrounds responding in social and behavioral studies. Many scholars have called for substantial increases in the sample sizes of psychology studies to eliminate the publication of spurious chance findings due to a random error and inflated effect size estimates resulting from the premature stopping of data collection. While some of the variability can be reduced through better measures and more uniform procedures, we believe that much of the random error is inherent to studies of complex phenomena. As we discussed previously, many psychological constructs cannot be measured precisely or reliably. Moreover, research settings, procedures, and participants feature a multitude of moving parts that can impact behavioral responding. Consequently, high levels of noise may be characteristic of psychological research.

In line with the tenets of scientific determinism, we believe that, in principle, “exact” replication studies should obtain the same findings as the original study. However, many scholars have pointed out (e.g., Rosenthal, 1991 ; Fabrigar and Wegener, 2016 ) that “exact” replications in psychology are never exactly the same. Even when researchers diligently strive to duplicate the manipulations and measures of a study, there are inevitably variations in the interactions, participants, laboratories, institutions, and cultures. Moreover, even when the procedures are identical, they often vary in terms of how they are perceived by participants ( Stroebe and Strack, 2014 ). While the phenomena studied in all fields are affected by context, the extraneous factors operating in studies of physical effects may generally be fewer in number and more readily controlled.

Finally, replications are often conducted months and years following the publication of a study and after momentous political, social, and environmental events have altered attitudes and the societal milieu. While these events may not change the effects of temperature on the physical state of water, they do influence many of the complex processes and behaviors that are studied in psychological science. Conceptual replications, of course, are even less likely to reproduce the findings of a study (e.g., Earp and Trafimow, 2015 ). As we discussed previously, many of the theoretical constructs studied in psychological research are abstractly and loosely defined. Consequently, operationalizations of the same construct are often very dissimilar from one another.

In keeping with arguments about the effects of context on reproduction, evidence indicates that one of the most fundamental reasons why replication rates in psychology are low is the hetereogeneity of study effect sizes (e.g., McShane et al., 2019 ). As Stanley et al. (2018 , p. 1325) state, “Heterogeneity… makes it unlikely that the typical psychological study can be closely replicated when replication is defined as study-level null hypothesis significance testing…” The variability of particular relations and effects varies tremendously as a function of the complexity of the phenomena under investigation and scientific discipline. Because complex phenomena commonly change across context and time, a broader range of effect sizes may be generated by social and behavioral studies than by research in fields such as chemistry.

While an average or typical effect size can be calculated for a set of studies, any notions that there is a singular universal “true effect” or “true correlation” characterizing complex phenomena across studies conducted in different settings and times are generally wishful thinking. The expectation that the same mathematical relation will be uncovered by each and every replication study in psychology is at odds with the empirical data. Thus, Patil et al. (2016 , p. 540) point out that when replication studies are conducted, researchers should not expect “the same numbers will result for a host of reasons including both natural variability and changes in the sample population, methods, or analysis techniques.”

While there are few, if any, broad studies that have systematically compared effect sizes across disciplines (for comparisons between psychology and other disciplines, see Hedges, 1987 ; Ferguson, 2009 ), research shows that the relations studied in psychological research are commonly weak (e.g., Hemphill, 2003 ; Schafer and Schwarz, 2019 ) and, hence, less reproducible ( Ioannidis, 2005 ). When the finding of a published study is weak to the point that it just meets the threshold for “significance,” a sizeable portion of the replication studies will be non-significant. If reproduction failure is defined at all in terms of failing to attain statistical significance, this is evidence that the original findings were false and a Type I error. However, the negative results could simply be the consequence of conditions or contexts that are less conducive to the effect.

A number of scholars have pointed out that when even replication studies fail to achieve statistical significance, they typically show the same patterns or directional trends and, thus, to a large degree “reproduce” the original findings. For example, an analysis of the studies of the first Many Labs Reproducibility Project (2014) by Patil et al. (2016) showed that 77% of the replication effect sizes reported were within a 95% prediction interval (one-way) based on the original effect size. All of this has led to informative discussion and some disagreement about what constitutes reproduction (e.g., Maxwell et al., 2015 ; Open Science Collaboration, 2015 ; Patil et al., 2016 ). Gelman (2018) argues that we should not even characterize replications as successes or failures.

If a study finding falls squarely in the middle of the distribution of possible effect sizes, half of the replication findings might exhibit stronger correlations or effects. However, this is not the pattern that is typically observed across replication studies. The majority of the relations observed in replication studies are weaker than those of the originally published research. As we discuss shortly, questionable research practices including the “publication bias” foster the reporting of studies that are hard to reproduce.

Questionable Research Practices

The high frequency of replication failure in psychological science is generally attributed to questionable research practices or outright fraud rather than the complexity of the study phenomena. Scholars generally believe that studies are reproduced less often in disciplines such as psychology and medicine than in the natural sciences because misconduct is more commonplace.

Research has uncovered a number of specific practices such as rounding down p -values and claiming to have predicted an unexpected result that can lead to erroneous conclusions and the publication of unreproducible findings. Experimenter bias and demand effects may also contribute to the unwarranted confirmation of the hypothesis under investigation (e.g., Rosenthal and Rosnow, 2009 ; Klein et al., 2012 ). More broadly, scholars have suggested that there is a “publication bias” in favor of positive results (e.g., Ferguson and Heene, 2012 ) that contributes to the “file drawer problem” (e.g., Rosenthal, 1979 ; Franco et al., 2014 ). Together, these contribute to inflated estimates of the size of relations or effects in psychology studies. This is evidenced not only by failures to replicate but by analyses showing that there are significantly more reports of studies that reject the null hypothesis than is consistent with a power or Bayesian analysis of the body of work (e.g., Renkewitz et al., 2011 ; Etz and Vandekerckhove, 2016 ).

Inappropriate research practices are certainly a major problem in psychological science. For example, a survey study by Fiedler and Schwarz (2016) showed that while data fabrication is rare, a sizeable percentage of psychologists admit to specific practices such as deciding whether to continue data collection after testing the significance of the results (over 40%) at least once in their careers. However, there is little direct evidence showing that inappropriate research practices are more common in psychology than in other fields. For example, there are no studies showing that psychologists are more likely than other scientists to treat post-hoc hypotheses as a priori hypotheses. The fact is that very few studies have systematically compared the prevalence of misconduct in psychology vs. other disciplines. Instead, scholars and journalists have assumed that clearly inappropriate research practices are more rampant in psychological science than in other fields because the frequency of reproduction in the field is low and effect size estimations are inflated. They have similarly assumed that the motivations and processes contributing to misconduct are more prevalent in psychological science.

However, it is silly to think that the incentives to engage in inappropriate practices are greater in psychology than in other disciplines. As we all know, the potential financial rewards that ostensibly motivate scientists to engage in misconduct are far lower in psychology than in fields such as chemistry and engineering. Any suggestions that greater scientific fame and stature can be achieved in psychology than in other fields are similarly ridiculous. Fox (1990) argues that, because the stakes are lower, data fabrication and falsification are less frequent in the social and behavioral sciences. Some scholars ( Weinstein, 1979 ; Braxton and Hargens, 1996 ) have also suggested that the policing of professional behavior is higher in “low consensus” or “soft” sciences than in “high consensus” sciences, which would further diminish the inclination to fudge.

Finally, there are no data to suggest that psychologists have lower ethical standards, less training in correct practices, or lower knowledge of research pitfalls. If anything, psychologists are more likely than other scientists to be informed about research bias and misconduct because these are inherently fascinating and important behavioral topics that are relevant to core areas of the field, such as judgment and decision making, and moral psychology. We speculate that one of the reasons why there has been an avalanche of studies on replication in psychological science is because it is such a rich research topic. We would further argue that psychologists are much better equipped than scientists and scholars in other fields to understand inferential errors in science and violations of research norms.

While there is little direct evidence showing that psychologists are generally more guilty of misconduct than other scientists, there are two specific methodological practices affecting reproducibility that appear to be more prevalent in psychology than in many other fields. Importantly, both of these problems arise, in part, because of the complexity of social and behavioral phenomena.

As we discussed previously, one contributor to replication failures in psychology has been underpowered studies (e.g., Cohen, 1962 ). However, the samples of many studies have been too small largely because of the inordinate noise that is inherent to complex psychological phenomena. Thus, it is complexity that has helped to make sampling practices in the field questionable. In our view, undersized samples in past studies should not be regarded as misconduct because most researchers were abiding by the prevailing norms for sampling.

The publication bias contributing to inflated effect sizes and replication failures (e.g., Ferguson and Heene, 2012 ) also appears to be more problematic and pronounced in psychology than in the physical sciences. The prevailing norm in all scientific disciplines is to engage in a confirmatory search and publish positive findings ( Fanelli, 2012 ; Fanelli et al., 2017 ). Non-relations in nature are infinite in number and are typically theoretically uninteresting. While the publication bias tends to be universal, its impact on reproducibility varies substantially across fields. Researchers in simpler disciplines generally get away with focusing on positive effects because they are more invariant and reproducible. In contrast, positive findings in psychology and other social and behavioral sciences are less likely to be reproduced because of the heterogeneity of the relations. Hence, the publication bias is far more problematic in complex fields.

As we discussed previously, the norm to seek and publish positive results also appears to be stronger in psychological science ( Fanelli, 2010 ) because of the complexity of behavioral phenomena. Science often progresses through the initial demonstration of an effect followed by an examination of the boundary conditions. This is particularly necessary in complex disciplines where the causal relations are lacking in uniformity. Typically, psychologists set up a study in a way that maximizes the likelihood of demonstrating a hypothesized relation in a particular research context. This has been aptly pointed out by Fiedler (2011) who persuasively argued that researchers “select stimuli, task settings, favorable boundary conditions, dependent variables and independent variables, treatment levels, moderators, mediators, and multiple parameter settings in such a way that empirical phenomena become maximally visible and stable. In general, paradigms can be understood as conventional setups for producing idealized, inflated effects” (p. 163). Designing research that optimizes the chances of showing an effect often leads to the publishing of findings that are atypical and not reproducible across all contexts. However, as we suggested previously, it is impossible to construct a psychology study that is representative of the universe with respect to the settings, participants, and operationalizations because of the complexity of these variables. For example, in a study of persuasion, what message could be presented to participants that is representative of all possible communications? How would one choose a physical environment for a study that is representative of all the physical environments in the world? Instead, psychological scientists demonstrate a phenomenon using select samples of the important study components with the expectation that subsequent research will investigate the conditions in which the effect occurs and does not occur. While the pronounced bias toward positive results in complex fields such as psychology contributes to inflated initial effect size estimates, the approach is necessary given the uninformativeness of most null findings.

The Importance of Replication Studies

The replication crisis in psychology has advanced the discipline in numerous ways. The documentation and analysis of reproduction failures has led to the implementation of new research norms and procedures, many of which are vital to the integrity and future of the field (e.g., Asendorpf et al., 2013 ; Funder et al., 2014 ; Nelson et al., 2018 ). One of the most important developments in psychology has been the sanctioning of replication studies and negative results. Researchers are conducting and publishing replication studies to restore confidence in the field and to advance theory. Most scientists believe that the primary purpose of replication studies is to determine whether a finding is real and duplicable. However, when the study phenomena are complex, any expectations of exact quantitative reproduction across studies are unrealistic and contrary to the data on the hetereogeneity of replication findings (e.g., Patil et al., 2016 ; Stanley et al., 2018 ). Following McShane et al. (2019) , we believe that one of the primary aims of replication studies should be to help profile a correlation or effect. Because of the complexity of most social and behavioral topics, it is important to examine the variability and robustness of the test relation ( Stanley and Spence, 2014 ) within a range of contexts. Replication studies and meta-analyses can also provide a better estimate of the typical effect size and diminish the publication bias ( Stanley et al., 2018 ). Finally, follow-up research may help to uncover important moderators of a correlation or effect. Because of the importance of examining the variability of effect sizes, it may be far more useful to conduct multiple studies with reasonably sized samples in different labs and contexts than conducting a single study with a mega-sample in a particular lab.

What Do Replication Studies Say About Generalization?

Psychological science has been understandably preoccupied with replication for the past decade. However, the broader and more important topic that has been overlooked in all of the hubbub is the generality of research findings in the field. While replication is important, it is just part of the more central issue of generalization.

Replication studies in complex fields can be seen as tests of whether the results of a study generalize to highly similar conditions or contexts using the same procedures. The empirical data starkly illuminate the formidable beast that social and behavioral scientists attempt to capture in their research. The variability characterizing complex phenomena is so great that study findings often do not generalize to even highly similar conditions.

New discoveries in psychological science are often touted for their importance and potential applications. However, when the topic is complex, the studies that follow commonly uncover boundary conditions that severely limit the scope of the findings. Moreover, there is typically an array of unspoken moderators that are not explicit in the conceptualization that further diminish the generality of the relation or effect. In the end, the findings and applications are often proven to be much narrower than researchers initially hoped and believed.

The Most Straightforward Conclusion

The belief of many scientists and journalists has been that questionable practices and errant researchers are responsible for the reproduction crisis in psychology. Many research findings have been assumed to be fake or false because they have not been consistently reproduced in follow up studies. Paralleling this, some researchers have been accused of misconduct and incompetence because their studies have not been reliably duplicated.

While some published findings are spurious, there is a more straightforward explanation for the numerous replication failures in psychological science that does not entail the wholesale dismissal of the data. Following Stanley et al. (2018) , and other researchers, we believe that divergent findings are an indication of the variability of the study effects and correlations. As McShane et al. (2019 , p. 102) state: “heterogeneity is not only the norm but also cannot be avoided in psychological research – even if every effort is taken to eliminate it.” More broadly, we believe that replication failures reflect the complexity and limited reproducibility of psychological effects. This conclusion is supported not only by the data but also by theory. Social and behavioral phenomena are less general, more mutable, and weaker than those investigated in the physical sciences. As a consequence, the correlations and effects observed in studies are much less consistent across context. In our view, the conclusion that most replication failures are due to fake or false findings is dubious because it implies that the variability in results will disappear when more ethical and rigorous practices are put into place.

Replication has long been regarded as a cornerstone of research. However, the lofty benchmarks that prevail in science have been based largely on studies of simpler topics. The research on replication in psychology provides strong empirical evidence that the standards that are applied in science should vary as a function of the complexity of the study phenomena and discipline.

Why Social and Behavioral Science Lacks Rigor

“Because of the success of science, there is a kind of a pseudo-science. Social science is an example of a science which is not a science. They follow the forms. You gather data, you do so and so and so forth, but they do not get any laws, they have not found out anything. They have not got anywhere – yet. Maybe someday they will, but it’s not very well developed… Now, I might be quite wrong. Maybe they do know all these things. But I do not think I’m wrong. See, I have the advantage of having found out how hard it is to get to really know something, how careful you have to be about checking your experiments, how easy it is to make mistakes and fool yourself. I know what it means to know something. And therefore, I see how they get their information. And I cannot believe that they know when they have not done the work necessary, they have not done the checks necessary, they have not done the care necessary. I have a great suspicion that they do not know…” (Nobel Laureate Richard Feynman quoted in Johnson, 2009 ).

The replication crisis in science has triggered a renewed focus on rigor in research. Like most broad behavioral constructs, “rigor” has been defined in different ways. The National Institutes of Health (2015) defines scientific rigor as “the strict application of the scientific method to ensure robust and unbiased experimental design, methodology, analysis, interpretation and reporting of results. This includes full transparency in reporting experimental details so that others may reproduce and extend the findings.” Some researchers define rigor more abstractly as “theoretical or experimental approaches undertaken in a way that enhances confidence in the veracity of their findings, with veracity defined as truth or accuracy” ( Casadevall and Fang, 2016 ). Both of these definitions focus on methods and approaches that increase the validity of scientific findings. However, “rigor” is also commonly defined more behaviorally as being careful, exact, and precise and adhering to strict standards of research.

Regardless of how the term is defined, there seems to be agreement that the social and behavioral sciences are less rigorous than the natural sciences. As the quote by the eminent physicist Richard Feynman suggested, many people believe that fields such as psychology and sociology are “pseudo sciences” that “have not done the checks necessary” and “have not done the care necessary.” We wish we could disagree with the critics and point smartly to data in support of the contrary. However, objective indices, such as reproduction rates and reliability, seem to verify that psychological science is generally lacking in rigor.

Paralleling other methodological issues, we believe that the limited rigor in fields such as psychology is partly attributable to logistical, financial, institutional, and ethical constraints and is largely attributable to the research phenomena. As we discuss shortly, the complexity of social and behavioral topics often precludes the implementation of highly rigorous methods of study. While the practices fall short of the standards of the physical sciences, we believe that they are efficient given the inherent difficulties of conceptualizing and studying complex phenomena.

Modest Goals

Research on motivation suggests that individual performance and achievement often begins with goal setting. People set goals for themselves that are lofty or lowly or in between. Research ( Locke and Latham, 1990 ) has shown that the types of goals and expectations that people begin with affects the success that they attain. Persons who set easy goals for themselves tend to accomplish less than persons with higher aspirations and expectations.

We believe that the lack of rigor in the social and behavioral sciences begins with the modest goals that guide research. For decades, the standard approach taken by most studies in psychology has been null hypothesis testing. Research has sought to provide evidence that the null hypothesis is not true, and there is a relation between variables or a difference between conditions. More broadly, psychological science has been largely content to identify causes and effects without specifying the exact mathematical relation between them. As we shall discuss, basic studies in psychology rarely test parameter estimates. That is, they infrequently attempt to verify specific estimations of the strength of a relation or the magnitude of an effect.

Null hypothesis testing has been rightfully criticized on a number of grounds (e.g., Meehl, 1978 ; Gigerenzer, 1998 ). However, one of the biggest problems is that it encourages sloppiness ( Szucs and Ioannidis, 2017 ). Null hypothesis testing aimed at demonstrating a mere difference or relation disincentivizes researchers to be precise and careful.

Building on Weakness

Henry Ford, it is said, commissioned a survey of the car scrap yards of America to find out if there were parts of the Model T Ford, which never failed. His inspectors came back with reports of almost every kind of breakdown: axles, brakes, pistons – all were liable to go wrong. But, they drew attention to one notable exception, the kingpins of the scrapped cars invariably had years of life left in them. With ruthless logic, Ford concluded that the kingpins on the Model T were too good for their job and ordered that in the future they should be made to an inferior specification ( Humphrey, 1976 , p. 303).

Although the accuracy of this tale about the business acumen of Henry Ford has been vigorously disputed (e.g., Hawks, 2005 ), a gem of an idea is presented that is relevant to many industries and endeavors including science. The output or productivity of many systems is limited by the weakest or worst performing parts. In some instances, expenditures may be decreased and efficiency may be increased by reducing the quality of the stronger or better performing components. Similarly, there is little utility in increasing the quality of a component if the general output is capped by the weakness of other parts.

Research studies consist of many different “parts” such as the sample, design, procedures, manipulations, measures, and analyses. As we reviewed previously, the rigor of many of these parts is compromised by the complexity of social and behavioral topics. Psychological scientists are generally aware of the weaknesses of various components of their studies. Moreover, many know that the generalizability of their research is severely restricted by the lack of representativeness of the study components and the variability of the research phenomena. We speculate that psychologists and other social and behavioral scientists are practical and efficient. They realize that the output of their research, that is, the conclusions that can be drawn, are limited by the weakest facets of their studies. Cognizant of these shortcomings, they efficiently minimize investing excessive time, effort, and funding on important components of their research. That is, they build some elements of their studies to an “inferior specification.” For example, why bother developing a precisely calibrated experimental manipulation when the scales of the measurement instruments are ordinal? Why expend resources on getting an exacting estimate of the sample population when the findings will not generalize across contexts?

When this efficiency is combined with the modest goal of showing that there is a relation between variables or a difference between conditions in null hypothesis testing, the rigor of research invariably suffers. The collective effect of these disincentives is to generally diminish the carefulness and precision of studies in the field. However, limiting rigor in this way is often the most sensible way of conducting research in psychological science. In the end, the most that researchers are typically able to conclude theoretically from their studies is that there is a relation or difference. Consequently, psychologists often do just enough to demonstrate a relation or difference.

Obviously, psychological science does not always work in this way. Research components sometimes have to be overbuilt to compensate for key weaknesses of a study. In some instances, the manipulations are so weak or the measures are so insensitive that the standards for other components have to be raised just to show a relation or effect. For example, current neuroimaging techniques are so noisy, crude, and expensive that study manipulations and procedures often have to be just right to register an effect on the measurements of the magnet.

Easy Solutions

Some readers might conclude that the general lack of rigor in psychology can be readily resolved by raising standards in the field. Rather than merely showing a difference or effect in a study, psychologists should test the parameter estimates of their theories. The problem with this solution, of course, is that most basic theories in psychological science are qualitative. Hence, null hypothesis testing is perfectly suited for the vague predictions that are afforded by most basic theories in the field. This suggests another easy solution to the modest goals that disincentivize rigor in psychology: researchers should develop better theories. However, as Sanbonmatsu and Johnston (2019) point out, most basic theories in social and behavioral science are necessarily qualitative and vague because of the variability of the causal relations that are studied.

The mandate to present effect sizes in research reports (e.g., American Psychological Association, 2001 ) may be helping to improve the rigor of psychological studies. Although Cohen (1994 , p. 1001) advised against looking “for a magical alternative to null hypothesis testing,” an increased focus on the size of relations or effects may incentivize researchers to upgrade their manipulations and measures. The development and testing of quantitative models may also raise the methodological standards of psychological research. Rigor matters more in tests of the predictiveness of applied models because the data serve as the basis for parameter estimation. Nevertheless, the rigor of psychological science will always be lower than that of fields such as physics and chemistry because of the complexity of the study phenomena. Complexity diminishes rigor by creating chronic problems in sampling, measurement, treatment, and reproduction. Complexity further limits rigor in psychology by diminishing the specificity of the hypotheses that can be tested and the conclusions that can be drawn and by disincentivizing researchers to be precise in all of the components of their studies.

We believe that if human behavior were simpler phenomena characterized by greater uniformity in causal relations across time and context, psychological science would be a very different enterprise. The rigor of research in the field undoubtedly would be an order of magnitude higher. Unfortunately, the failure to recognize the inherent limitations imposed by the complexity of behavioral phenomena and the continual pressure to achieve unrealistic scientific standards has been a continual source of criticism, self-doubt, and turmoil in psychology.

Some Topics Are Harder to Study Than Others

Science is characterized by greater methodological diversity than ever before. As fields have progressed, the array of designs, analyses, and approaches used in research has grown tremendously. What has been made evident in recent decades is that there is no one correct way of practicing science. Different methods are needed to investigate different topics and to achieve different scientific aims. Research across disciplines has also taught us that some topics are much harder to study than others. The precise measures, exacting predictions, uniform experimental findings, and quantitative theories of classic work in physical science are simply not possible in most areas of scientific study. As a consequence, the standards that are used to evaluate methods, findings, and theories should be tethered to the complexity of the study topic. The characterization of psychology and other social and behavioral disciplines as “fake science” reflects complete ignorance of the numerous parameters that shape scientific practice and achievement.

The standards that guide social and behavioral research are not “lower,” less demanding, or less scientific than those of other disciplines. Rather, they are often different because of the complexity of the study phenomena and other constraints. For example, the singular accounts that sometimes characterize theory in other fields generally do not work in the social and behavioral sciences. As we discussed previously, explanation and prediction in disciplines, such as economics and psychology, frequently requires the investigation of multiple causes and interactions, sophisticated statistical tests to parse out the respective contributions, and modeling of the complex relations. Another example of different standards is in sampling where representativeness is a common issue that necessitates special techniques and large numbers.

In contrasting psychology with the natural sciences, we focused on differences and neglected the parallels between fields. However, it is important to recognize that the methodological challenges that we reviewed are not unique to the social and behavioral sciences. Difficulties in measurement, reproducibility, communication, and rigor characterize the study of complex phenomena in all disciplines. In the physical sciences, researchers commonly investigate topics that are difficult to conceptualize, measure, and predict. As Bringmann and Eronen (2016 , p. 38) point out in their discussion of measurement practices in physics vs. psychology:

We believe that the differences are a matter of degree, and not as categorical as is often supposed. For example, although properties such as length or weight can be measured in a relatively direct and straightforward way, the same does not apply to phenomena such as the weak nuclear force or the background radiation of the universe. Such phenomena (which includes most phenomena studied in contemporary physics) can be measured only indirectly, and have no straightforward operationalizations ( Kyburg, 1984 ).

Hedges (1987) provocative but limited analysis of replications in physics and psychology in physics and psychology “suggests that the results of physical experiments may not be strikingly more consistent than those of social or behavioral experiments.” Finally, physicists have increasingly turned to modeling and simulation because many of the phenomena they are investigating are not subject to reductionist analysis and representation by tight mathematical theories ( Jogalekar, 2013 ). Thus, the differences in methods, procedures, findings, and theories that we have reviewed are more a function of the scientific endeavor and subject matter than discipline. As scientists in all fields take on phenomena of greater complexity, they are increasingly encountering the challenges that are inherent to the study of human behavior.

Author Contributions

DS and JB conceptualized and wrote the paper. EC conducted the archival research that served as the groundwork for the paper. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank Russell H. Fazio for his insightful comments on an earlier version of this paper.

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Hypothesis | Definition, Meaning and Examples

Hypothesis is a hypothesis is fundamental concept in the world of research and statistics. It is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables.

Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion . Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what hypothesis is, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

Table of Content

What is Hypothesis?

Characteristics of hypothesis, sources of hypothesis, types of hypothesis, functions of hypothesis, how hypothesis help in scientific research.

Hypothesis is a suggested idea or an educated guess or a proposed explanation made based on limited evidence, serving as a starting point for further study. They are meant to lead to more investigation.

It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes. Example: Studying more can help you do better on tests. Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together. Example: How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live. A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing. Example: Drinking more sweet drinks is linked to a higher body weight score. Too much stress makes people less productive at work.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes. Example: Drinking caffeine can affect how well you sleep. People often like different kinds of music based on their gender.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information. Example: The average test scores of Group A and Group B are not much different. There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one. Example: Patients on Diet A have much different cholesterol levels than those following Diet B. Exposure to a certain type of light can change how plants grow compared to normal sunlight.

Statistical Hypothesis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only. Example: The average smarts score of kids in a certain school area is 100. The usual time it takes to finish a job using Method A is the same as with Method B.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely. Example: Having more kids go to early learning classes helps them do better in school when they get older. Using specific ways of talking affects how much customers get involved in marketing activities.

Associative Hypothesis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing. Example: Regular exercise helps to lower the chances of heart disease. Going to school more can help people make more money.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change. Example: Playing violent video games makes teens more likely to act aggressively. Less clean air directly impacts breathing health in city populations.

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge . It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.

The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .

The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.

Hypothesis – FAQs

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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• Review Article
• Published: 04 September 2024

The pathogenesis of IgA nephropathy and implications for treatment

• Chee Kay Cheung 1 , 2 ,
• Suceena Alexander 3 ,
• Heather N. Reich 4 ,
• Haresh Selvaskandan 1 , 2 ,
• Hong Zhang 5 &
• Jonathan Barratt   ORCID: orcid.org/0000-0002-9063-7229 1 , 2  

Nature Reviews Nephrology ( 2024 ) Cite this article

Metrics details

• Autoimmunity
• IgA nephropathy
• Therapeutics

IgA nephropathy (IgAN) is a common form of primary glomerulonephritis and represents an important cause of chronic kidney disease globally, with observational studies indicating that most patients are at risk of developing kidney failure within their lifetime. Several research advances have provided insights into the underlying disease pathogenesis, framed by a multi-hit model whereby an increase in circulating IgA1 that lacks galactose from its hinge region — probably derived from the mucosal immune system — is followed by binding of specific IgG and IgA antibodies, generating immune complexes that deposit within the glomeruli, which triggers inflammation, complement activation and kidney damage. Although treatment options are currently limited, new therapies are rapidly emerging that target different pathways, cells and mediators involved in the disease pathogenesis, including B cell priming in the gut mucosa, the cytokines APRIL and BAFF, plasma cells, complement activation and endothelin pathway activation. As more treatments become available, there is a realistic possibility of transforming the long-term outlook for many individuals with IgAN.

IgA nephropathy (IgAN) is an important cause of progressive kidney disease and kidney failure globally, with most patients being at risk of developing kidney failure within their lifetime.

Advances in the understanding of the pathogenesis of IgAN have highlighted an (auto)immune basis for the disease, with increased circulating levels of galactose-deficient IgA1 (Gd-IgA1) being associated with the presence of IgA and IgG antibodies specific to these IgA1 O -glycoforms.

The circulating Gd-IgA1 that forms immune complexes and is deposited within the glomeruli in IgAN is probably mucosal in origin.

The presence of elevated levels of Gd-IgA1 alone is insufficient to trigger IgAN; genetic and epigenetic factors contribute to the susceptibility of developing IgAN and the risk of progressive disease.

Several therapies that target mucosal B cell priming, B cell production of Gd-IgA1, complement activity and the endothelin system are in development for the treatment of IgAN.

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Mayer IgA Nephropathy Laboratories, Department of Cardiovascular Sciences, University of Leicester, Leicester, UK

Chee Kay Cheung, Haresh Selvaskandan & Jonathan Barratt

John Walls Renal Unit, University Hospitals of Leicester NHS Trust, Leicester, UK

Department of Nephrology, Christian Medical College, Vellore, India

Suceena Alexander

Department of Medicine, Division of Nephrology, University of Toronto, University Health Network, Toronto, ON, Canada

Heather N. Reich

Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, P. R. China

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All authors researched data for the article, made substantial contributions to discussions of the content and wrote, reviewed or edited the manuscript before submission.

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C.K.C. reports receiving consulting and speaker fees from Alexion, Alpine Immune Sciences, Calliditas, Chinook, CSL Vifor, George Clinical, Novartis, Otsuka, Stada, Travere Therapeutics, Vera Therapeutics; receiving grant support from Travere Therapeutics; and being on data-monitoring committees for Roche and Alpine Immune Sciences. H.N.R. has provided consultation for Calliditas, Chinook, Novartis and Omeros, and provided a conference lecture supported by Travere Therapeutics; serves on the steering committee of IgA studies for Calliditas and Chinook (a Novartis company); has attended advisory meetings for Otsuka, Pfizer and Eledon; is a clinical trial site investigator for Calliditas, Omeros and Alnylam; and directs the Louise Fast Foundation fellowship. H.Z. is employed by Peking University First Hospital and reports receiving consultancy fees for being a Steering Committee member from Novartis, Omeros, Calliditas, Chinook and Otsuka; and having participated in symposia or panel discussions and received honoraria for scientific presentations from Omeros and Novartis. J.B. reports consultancy for Alebund, Alnylam, Alpine, Argenx, Astellas, BioCryst, Calliditas, Chinook, Dimerix, HiBio, Kira, Novartis, Omeros, Otsuka, Q32 Bio, Roche, Sanofi, Takeda, Travere Therapeutics, Vera Therapeutics, Vifor and Visterra; research funding from Argenx, Calliditas, Chinook, Galapagos, GlaxoSmithKline, Novartis, Omeros, Travere Therapeutics and Visterra; a role on the Editorial Boards of CJASN , Clinical Science , Glomerular Diseases and Kidney International ; and an advisory or leadership role as Treasurer of the International IgA Nephropathy Network. The other authors declare no competing interests.

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Cheung, C.K., Alexander, S., Reich, H.N. et al. The pathogenesis of IgA nephropathy and implications for treatment. Nat Rev Nephrol (2024). https://doi.org/10.1038/s41581-024-00885-3

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Published : 04 September 2024

DOI : https://doi.org/10.1038/s41581-024-00885-3

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