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One pair of scientists thought they’d discovered a new antiviral protein buried inside skin cells. Another research team saw early hints suggesting that the flu virus might cooperate to boost infections in humans. And a nationwide team of clinicians thought that high doses of certain vitamins might prevent cancer.

These studies don’t have much to do with each other, except that the researchers had all based their hypotheses on convincing earlier data.

And those hypotheses were all wrong.

The hypothesis is a central tenet to scientific research. Scientists ask questions, but a question on its own is often not sufficient to outline the experiments needed to answer it (nor to garner the funding needed to support those experiments).

So researchers construct a hypothesis, their best educated guess as to the answer to that question.

How a hypothesis is formed

Technically speaking, a hypothesis is only a hypothesis if it can be tested. Otherwise, it’s just an idea to discuss at the water cooler.

Researchers are always prepared for the possibility that those tests could disprove their hypotheses — that’s part of the reason they do the studies. But what happens when a beloved idea or dogma is shattered is less technical, less predictable. More human.

In some cases, a disproven hypothesis is devastating, said Swedish Cancer Institute and Fred Hutchinson Cancer Research Center public health researcher Dr. Gary Goodman, who led one of those vitamin studies. In his case, he was part of a group of cancer prevention researchers who ultimately showed that high doses of certain vitamins can increase the risk of lung cancer — an important result, but the opposite of what they thought they would prove in their trials.

But for some, finding a hypothesis to be false is exhilarating and motivating.

Herpes hypothesis leads to surprise cancer-related finding

Dr. Jia Zhu , a Fred Hutch infectious disease scientist, and her research partner (and husband), Fred Hutch and University of Washington infectious disease researcher Dr. Tao Peng, thought they’d found a new antiviral in herpes simplex virus type 2, or HSV-2, in part because they’ve been focused on that virus — and its interaction with human immune cells — for decades now, together with Dr. Larry Corey , virologist and president and director emeritus of Fred Hutch.

A few years ago, Zhu and Peng found that a tiny, mysterious protein called interleukin-17c is massively overproduced by HSV-infected skin cells. Maybe it was an undiscovered antiviral protein, the virologists thought, made by the skin cells in an attempt to protect themselves. They spent more than half a year pursuing that hypothesis, conducting experiment after experiment to see if IL-17c could block the herpes virus from replicating. It didn’t.

Zhu pointed to a microscopic image of a biopsy from a person with HSV, captured more than 10 years ago where she, Corey and their colleagues first discovered that certain T cells, a type of immune cell, cluster in the skin where herpes lesions form. At the top of the colorful image, a layer of skin cells stained blue is studded with orange-colored T cells. Beneath, green nerve endings stretch their branch-like fibers toward the infected skin cells.

“This is my favorite image, but we all focused on the top,” the skin and immune cells, Zhu said. “We never really paid attention to the nerves.”

"You take an approach and then you just have to let the science drive." — Dr. Jia Zhu, infectious disease researcher

Finally, Peng discovered that the nerve fibers themselves carry proteins that can interact with the IL-17c molecule produced in infected skin cells — and that the protein signals the nerves to grow, making it one of only a handful of nerve growth factors identified in humans.

The researchers are excited about their serendipitous finding not just because it’s another piece in the puzzle of this mysterious virus, which infects one in six teens and adults in the U.S. They also hope the protein could fuel new therapies in other settings — such as neuropathy, a type of nerve damage that is a side effect of many cancer chemotherapies.

It’s a finding they never would have anticipated, Zhu said, but that’s often the nature of research.

“You do have a big picture, you know the direction. You take an approach and then you just have to let the science drive,” she said. “If things are unexpected, maybe just explore a little bit more instead of shutting that door.”

Flu hypothesis leads to a new mindset and avenue of research

Sometimes, a mistaken hypothesis has less to do with researchers’ preconceptions and more to do with the way basic research is conducted. Take, for example, the work of Fred Hutch evolutionary biologist Dr. Jesse Bloom , whose laboratory team studies how influenza and other viruses evolve over time. Many of their experiments involve infecting human cells in a petri dish with different strains of the flu virus and seeing what happens.

A few years ago, Bloom and University of Washington doctoral student Katherine Xue made an intriguing discovery using that system: They saw that two variants of influenza H3N2 (the virus that’s wreaking havoc in the current flu season) could cooperate to infect cells better together than either version could alone.

The researchers had only shown that viral collaboration in petri dishes in the lab, but they had reason to think it might be happening in people, too. For one, the same mix of variants was present in public databases of samples taken from infected people — but those samples had also been grown in petri dishes in the lab before their genomic information was captured.

So Xue and Bloom sequenced those variants at their source, the original nasal wash samples collected and stored by the Washington State Public Health Laboratories . They found no such mixture of variants from the samples that hadn’t been grown in the laboratory — so the flu may not cooperate after all, at least not in our bodies. The researchers published their findings last month in the journal mSphere.

Scientists have to ask themselves two questions about any discovery, Bloom said: “Are your findings correct? And are they relevant?”

The team’s first study wasn’t wrong; the viruses do cooperate in cells in the lab. But the second question is usually the tougher one, the researchers said.

“There are a lot of differences, obviously, between viruses growing in a controlled setting in a petri dish versus an actual human,” Xue said.

She and Bloom aren’t too glum about their disproven hypothesis, though. That line of inquiry opened new doors in the lab, Bloom said.

Before Xue’s study, he and his colleagues exclusively studied viruses in petri dishes. Now, more members of his laboratory team are using clinical samples as well — an approach that is made possible by the closer collaborations between basic and clinical research at the Hutch, Bloom said.

Some of their findings in petri dishes aren’t holding true in the clinical samples. But they’re already making interesting findings about how flu evolves in the human body — including the discovery that how flu evolves in single people with unusually long infections can hint at how the virus will evolve globally, years later. They never would have done that study if they hadn’t already been trying to follow up their original, cooperating hypothesis.

“It opened this whole new way of trying to think about this,” Bloom said. “Our mindset has changed a lot.”

Prevention hypothesis flipped on its head

Fred Hutch and Swedish cancer prevention researcher Goodman and his epidemiology colleagues had good reason to think the vitamins they were testing in clinical trials could prevent lung cancer.

All of the data pointed to an association between the vitamins and a reduced risk of lung cancer. But the studies hadn’t shown a causative link — just a correlation. So the researchers set out to do large clinical trials comparing high doses of the vitamins to placebos.

In the CARET trial , which Goodman led and was initiated in 1985, 18,000 people at high risk of lung cancer (primarily smokers) were assigned to take either a placebo, vitamin A, beta-carotene (a vitamin A precursor) or a combination of the two supplements. Two other similar trials started in other parts of the world at around the same time also testing beta-carotene’s effect on lung cancer risk.

In a similar vein, at the same time, a small trial suggested that supplemental selenium decreased the incidence of prostate cancer. So in 2001, the SELECT trial launched through SWOG , a nationwide cancer clinical trial consortium, testing whether selenium or high-dose vitamin E or the combination could prevent prostate cancer. SELECT enrolled 35,000 men; Goodman was the study leader for the Seattle area.

Designing and conducting cancer prevention trials where participants take a drug or some other intervention is a tricky business, Goodman said.

“In prevention, most of the people you treat are healthy and will never get cancer,” he said. “So you have to make sure the agent is very safe.”

Previous studies had all pointed to the vitamins being safe — even beneficial. And the vitamins tested in the trials are all naturally occurring as part of our diets. Nobody thought they could possibly hurt.

But that’s exactly what happened. In the CARET study, participants taking the combination of vitamin A and beta-carotene had higher rates of lung cancer than those taking the placebo; other trials testing those vitamins saw similar results. And in the SELECT trial, those taking vitamin E had higher rates of prostate cancer.

All the trials had close monitoring built in and all were stopped early when the researchers saw that the cancer rates were trending the opposite way that they’d expected.

“It was just devastating when we learned the results,” Goodman said. “Everybody [who worked on the trial] was so hopeful. After all, we’re here to prevent cancer.”

When the CARET study stopped, Goodman and his team hired extra people to answer study participants’ questions and the angry phone calls they assumed they would get. But very few phone calls came in.

“They said they were involved in the study for altruistic reasons, and we got an answer,” he said. “One of the benefits of our study is that we did show that high doses of vitamins can be very harmful.”

That was an important finding, Goodman said, because the prevailing dogma at the time was that high doses of vitamins were good for you. Although these studies disproved that commonly held belief, even today not everyone in the general public buys that message.

Another benefit of that difficult experience: The bar for giving healthy people a supplement or drug with the goal of preventing cancer or other disease is much higher now, Goodman said.

“In prevention, [these studies] really changed people’s perceptions about what kind of evidence you need to have before you can invest the time, money, effort, human resources, people’s lives in an intervention study,” he said. “You really need to have good data suggesting that an intervention will be beneficial.”

rachel-tompa

Rachel Tompa is a former staff writer at Fred Hutchinson Cancer Center. She has a Ph.D. in molecular biology from the University of California, San Francisco and a certificate in science writing from the University of California, Santa Cruz. Follow her on Twitter @Rachel_Tompa .

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

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

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

There are 5 main steps in hypothesis testing:

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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6a.1 - introduction to hypothesis testing, basic terms section  .

The first step in hypothesis testing is to set up two competing hypotheses. The hypotheses are the most important aspect. If the hypotheses are incorrect, your conclusion will also be incorrect.

The two hypotheses are named the null hypothesis and the alternative hypothesis.

The goal of hypothesis testing is to see if there is enough evidence against the null hypothesis. In other words, to see if there is enough evidence to reject the null hypothesis. If there is not enough evidence, then we fail to reject the null hypothesis.

Consider the following example where we set up these hypotheses.

Example 6-1 Section  

A man, Mr. Orangejuice, goes to trial and is tried for the murder of his ex-wife. He is either guilty or innocent. Set up the null and alternative hypotheses for this example.

Putting this in a hypothesis testing framework, the hypotheses being tested are:

• The man is guilty
• The man is innocent

Let's set up the null and alternative hypotheses.

\(H_0\colon \) Mr. Orangejuice is innocent

\(H_a\colon \) Mr. Orangejuice is guilty

Remember that we assume the null hypothesis is true and try to see if we have evidence against the null. Therefore, it makes sense in this example to assume the man is innocent and test to see if there is evidence that he is guilty.

The Logic of Hypothesis Testing Section  

We want to know the answer to a research question. We determine our null and alternative hypotheses. Now it is time to make a decision.

The decision is either going to be...

• reject the null hypothesis or...
• fail to reject the null hypothesis.

Consider the following table. The table shows the decision/conclusion of the hypothesis test and the unknown "reality", or truth. We do not know if the null is true or if it is false. If the null is false and we reject it, then we made the correct decision. If the null hypothesis is true and we fail to reject it, then we made the correct decision.

So what happens when we do not make the correct decision?

When doing hypothesis testing, two types of mistakes may be made and we call them Type I error and Type II error. If we reject the null hypothesis when it is true, then we made a type I error. If the null hypothesis is false and we failed to reject it, we made another error called a Type II error.

Types of errors

The “reality”, or truth, about the null hypothesis is unknown and therefore we do not know if we have made the correct decision or if we committed an error. We can, however, define the likelihood of these events.

\(\alpha\) and \(\beta\) are probabilities of committing an error so we want these values to be low. However, we cannot decrease both. As \(\alpha\) decreases, \(\beta\) increases.

Example 6-1 Cont'd... Section  

A man, Mr. Orangejuice, goes to trial and is tried for the murder of his ex-wife. He is either guilty or not guilty. We found before that...

• \( H_0\colon \) Mr. Orangejuice is innocent
• \( H_a\colon \) Mr. Orangejuice is guilty

Interpret Type I error, \(\alpha \), Type II error, \(\beta \).

As you can see here, the Type I error (putting an innocent man in jail) is the more serious error. Ethically, it is more serious to put an innocent man in jail than to let a guilty man go free. So to minimize the probability of a type I error we would choose a smaller significance level.

Try it! Section  

An inspector has to choose between certifying a building as safe or saying that the building is not safe. There are two hypotheses:

• Building is safe
• Building is not safe

Set up the null and alternative hypotheses. Interpret Type I and Type II error.

\( H_0\colon\) Building is not safe vs \(H_a\colon \) Building is safe

Power and \(\beta \) are complements of each other. Therefore, they have an inverse relationship, i.e. as one increases, the other decreases.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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Responding to a Disproven or Failed Research Hypothesis

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When meeting with a disproven or failed hypothesis , after having expended so much time and effort, precisely how should researchers respond? Responding well to a disproven or failed hypothesis is an essential component to scientific research . As a researcher, it helps to learn ‘ research resilience ’: the ability to carefully analyse, effectively document and broadly disseminate the failed hypotheses, all with an eye towards learning and future progress. This article explores common reasons why a hypothesis fails, as well as specific ways you can respond and lessons you can learn from this. 

Note : This article assumes that you are working on a hypothesis (not a null hypothesis): in other words, you are seeking to prove that the hypothesis is true, rather than to disprove it. 

Reasons why a hypothesis is disproven/fails

Hypotheses are disproved or fail for a number of reasons, including:

• The researcher’s preconception is incorrect , which leads to a flawed and failed hypothesis.
• The researcher’s findings are correct, but those findings aren’t relevant .
• Data set/sample size may not be sufficiently large to yield meaningful results. (If interested, learn more about this here: The importance of having Large Sample Sizes for your research )
• The hypothesis itself lies outside the realm of science . The hypothesis cannot be tested by experiments for which results have the potential to show that the idea is false.

Responding to a disproved hypothesis

After weeks or even months of intense thinking and experimenting, you have come to the conclusion that your hypothesis is disproven. So, what can you do to respond to such a disheartening realisation? Here are some practical steps you can take.

• Analyse the hypothesis carefully, as well as your research.   Performing a rigorous, methodical ‘post-mortem’ evaluation of your hypothesis and experiments will enable you to learn from them and to effectively and efficiently share your reflections with others. Use the following questions to evaluate how the research was conducted: 
• Did you conduct the experiment(s) correctly? 
• Was the study sufficiently powered to truly provide a definitive answer?
• Would a larger, better powered study – possibly conducted collaboratively with other research centres – be necessary, appropriate or helpful? 
• Would altering the experiment — or conducting different experiments — more appropriately answer your hypothesis? 
• Share the disproven hypothesis, and your experiments and analysis, with colleagues. Sharing negative data can help to interpret positive results from related studies and can aid you to adjust your experimental design .
• Consider the possibility that the hypothesis was not an attempt at gaining true scientific understanding, but rather, was a measure of a prevailing bias .

Positive lessons to be gained from a disproved hypothesis

Even the most successful, creative and thoughtful researchers encounter failed hypotheses. What makes them stand out is their ability to learn from failure. The following considerations may assist you to learn and gain from failed hypotheses:

• Failure can be beneficial if it leads directly toward future exploration.
• Does the failed hypothesis definitively close the door on further research? If so, such definitive knowledge is progress.
• Does the failed hypothesis simply point to the need to wait for a future date when more refined experiments or analysis can be conducted? That knowledge, too, is useful. 
• ‘Atomising’ (breaking down and dissecting) the reasoning behind the conceptual foundation of the failed hypothesis may uncover flawed yet correctable thinking in how the hypothesis was developed. 
• Failure leads to investigation and creativity in the pursuit of viable alternative hypotheses, experiments and statistical analyses. Better theoretical or experimental models often arise out of the ashes of a failed hypothesis, as do studies with more rigorously attained evidence (such as larger-scale, low-bias meta-analyses ). 

Considering a post-hoc analysis

A failed hypothesis can then prompt you to conduct a post-hoc analysis. (If interested, learn more about it here: Significance and use of Post-hoc Analysis studies )

All is not lost if you conclude you have a failed hypothesis. Remember: A hypothesis can’t be right unless it can be proven wrong.  Developing research resilience will reward you with long-term success.

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Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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.

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Statistics By Jim

Making statistics intuitive

Failing to Reject the Null Hypothesis

By Jim Frost 69 Comments

Failing to reject the null hypothesis is an odd way to state that the results of your hypothesis test are not statistically significant. Why the peculiar phrasing? “Fail to reject” sounds like one of those double negatives that writing classes taught you to avoid. What does it mean exactly? There’s an excellent reason for the odd wording!

In this post, learn what it means when you fail to reject the null hypothesis and why that’s the correct wording. While accepting the null hypothesis sounds more straightforward, it is not statistically correct!

Before proceeding, let’s recap some necessary information. In all statistical hypothesis tests, you have the following two hypotheses:

• The null hypothesis states that there is no effect or relationship between the variables.
• The alternative hypothesis states the effect or relationship exists.

We assume that the null hypothesis is correct until we have enough evidence to suggest otherwise.

After you perform a hypothesis test, there are only two possible outcomes.

• When your p-value is greater than your significance level, you fail to reject the null hypothesis. Your results are not significant. You’ll learn more about interpreting this outcome later in this post.

Related posts : Hypothesis Testing Overview and The Null Hypothesis

Why Don’t Statisticians Accept the Null Hypothesis?

To understand why we don’t accept the null, consider the fact that you can’t prove a negative. A lack of evidence only means that you haven’t proven that something exists. It does not prove that something doesn’t exist. It might exist, but your study missed it. That’s a huge difference and it is the reason for the convoluted wording. Let’s look at several analogies.

Species Presumed to be Extinct

Lack of proof doesn’t represent proof that something doesn’t exist!

Criminal Trials

Perhaps the prosecutor conducted a shoddy investigation and missed clues? Or, the defendant successfully covered his tracks? Consequently, the verdict in these cases is “not guilty.” That judgment doesn’t say the defendant is proven innocent, just that there wasn’t enough evidence to move the jury from the default assumption of innocence.

Hypothesis Tests

The hypothesis test assesses the evidence in your sample. If your test fails to detect an effect, it’s not proof that the effect doesn’t exist. It just means your sample contained an insufficient amount of evidence to conclude that it exists. Like the species that were presumed extinct, or the prosecutor who missed clues, the effect might exist in the overall population but not in your particular sample. Consequently, the test results fail to reject the null hypothesis, which is analogous to a “not guilty” verdict in a trial. There just wasn’t enough evidence to move the hypothesis test from the default position that the null is true.

The critical point across these analogies is that a lack of evidence does not prove something does not exist—just that you didn’t find it in your specific investigation. Hence, you never accept the null hypothesis.

Related post : The Significance Level as an Evidentiary Standard

What Does Fail to Reject the Null Hypothesis Mean?

Accepting the null hypothesis would indicate that you’ve proven an effect doesn’t exist. As you’ve seen, that’s not the case at all. You can’t prove a negative! Instead, the strength of your evidence falls short of being able to reject the null. Consequently, we fail to reject it.

Failing to reject the null indicates that our sample did not provide sufficient evidence to conclude that the effect exists. However, at the same time, that lack of evidence doesn’t prove that the effect does not exist. Capturing all that information leads to the convoluted wording!

What are the possible implications of failing to reject the null hypothesis? Let’s work through them.

First, it is possible that the effect truly doesn’t exist in the population, which is why your hypothesis test didn’t detect it in the sample. Makes sense, right? While that is one possibility, it doesn’t end there.

Another possibility is that the effect exists in the population, but the test didn’t detect it for a variety of reasons. These reasons include the following:

• The sample size was too small to detect the effect.
• The variability in the data was too high. The effect exists, but the noise in your data swamped the signal (effect).
• By chance, you collected a fluky sample. When dealing with random samples, chance always plays a role in the results. The luck of the draw might have caused your sample not to reflect an effect that exists in the population.

Notice how studies that collect a small amount of data or low-quality data are likely to miss an effect that exists? These studies had inadequate statistical power to detect the effect. We certainly don’t want to take results from low-quality studies as proof that something doesn’t exist!

However, failing to detect an effect does not necessarily mean a study is low-quality. Random chance in the sampling process can work against even the best research projects!

If you’re learning about hypothesis testing and like the approach I use in my blog, check out my eBook!

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Reader Interactions

May 8, 2024 at 9:08 am

Thank you very much for explaining the topic. It brings clarity and makes statistics very simple and interesting. Its helping me in the field of Medical Research.

February 26, 2024 at 7:54 pm

Hi Jim, My question is that can I reverse Null hyposthesis and start with Null: µ1 ≠ µ2 ? Then, if I can reject Null, I will end up with µ1=µ2 for mean comparison and this what I am looking for. But isn’t this cheating?

February 26, 2024 at 11:41 pm

That can be done but it requires you to revamp the entire test. Keep in mind that the reason you normally start out with the null equating to no relationship is because the researchers typically want to prove that a relationship or effect exists. This format forces the researchers to collect a substantial amount of high quality data to have a chance at demonstrating that an effect exists. If they collect a small sample and/or poor quality (e.g., noisy or imprecise), then the results default back to the null stating that no effect exists. So, they have to collect good data and work hard to get findings that suggest the effect exists.

There are tests that flip it around as you suggest where the null states that a relationship does exist. For example, researchers perform an equivalency test when they want to show that there is no difference. That the groups are equal. The test is designed such that it requires a good sample size and high quality data to have a chance at proving equivalency. If they have a small sample size and/or poor quality data, the results default back to the groups being unequal, which is not what they want to show.

So, choose the null hypothesis and corresponding analysis based on what you hope to find. Choose the null hypothesis that forces you to work hard to reject it and get the results that you want. It forces you to collect better evidence to make your case and the results default back to what you don’t want if you do a poor job.

I hope that makes sense!

October 13, 2023 at 5:10 am

Really appreciate how you have been able to explain something difficult in very simple terms. Also covering why you can’t accept a null hypothesis – something which I think is frequently missed. Thank you, Jim.

February 22, 2022 at 11:18 am

Hi Jim, I really appreciate your blog, making difficult things sound simple is a great gift.

I have a doubt about the p-value. You said there are two options when it comes to hypothesis tests results . Reject or failing to reject the null, depending on the p-value and your significant level.

But… a P-value of 0,001 means a stronger evidence than a P-value of 0,01? ( both with a significant level of 5%. Or It doesn`t matter, and just every p-Value under your significant level means the same burden of evidence against the null?

I hope I made my point clear. Thanks a lot for your time.

February 23, 2022 at 9:06 pm

There are different schools of thought about this question. The traditional approach is clear cut. Your results are statistically significance when your p-value is less than or equal to your significance level. When the p-value is greater than the significance level, your results are not significant.

However, as you point out, lower p-values indicate stronger evidence against the null hypothesis. I write about this aspect of p-values in several articles, interpreting p-values (near the end) and p-values and reproducibility .

Personally, I consider both aspects. P-values near 0.05 provide weak evidence. Consequently, I’d be willing to say that p-values less than or equal to 0.05 are statistically significant, but when they’re near 0.05, I’d consider it as a preliminary result that requires more research. However, if the p-value is less 0.01, or even better 0.001, then that’s much stronger evidence and I’ll give those results more weight in my evaluation.

If you read those two articles, I think you’ll see what I mean.

January 1, 2022 at 6:00 pm

HI, I have a quick question that you may be able to help me with. I am using SPSS and carrying out a Mann W U Test it says to retain the null hypothesis. The hypothesis is that males are faster than women at completing a task. So is that saying that they are or are not

January 1, 2022 at 8:17 pm

In that case, your sample data provides insufficient evidence to conclude that males are faster. The results do not prove that males and females are the same speed. You just don’t have enough evidence to say males are faster. In this post, I cover the reasons why you can’t prove the null is true.

November 23, 2021 at 5:36 pm

What if I have to prove in my hypothesis that there shouldn’t be any affect of treatment on patients? Can I say that if my null hypothesis is accepted i have got my results (no effect)? I am confused what to do in this situation. As for null hypothesis we always have to write it with some type of equality. What if I want my result to be what i have stated in null hypothesis i.e. no effect? How to write statements in this case? I am using non parametric test, Mann whitney u test

November 27, 2021 at 4:56 pm

You need to perform an equivalence test, which is a special type of procedure when you want to prove that the results are equal. The problem with a regular hypothesis test is that when you fail to reject the null, you’re not proving that they the outcomes are equal. You can fail to reject the null thanks to a small sample size, noisy data, or a small effect size even when the outcomes are truly different at the population level. An equivalence test sets things up so you need strong evidence to really show that two outcomes are equal.

Unfortunately, I don’t have any content for equivalence testing at this point, but you can read an article about it at Wikipedia: Equivalence Test .

August 13, 2021 at 9:41 pm

Great explanation and great analogies! Thanks.

August 11, 2021 at 2:02 am

I got problems with analysis. I did wound healing experiments with drugs treatment (total 9 groups). When I do the 2-way ANOVA in excel, I got the significant results in sample (Drug Treatment) and columns (Day, Timeline) . But I did not get the significantly results in interactions. Can I still reject the null hypothesis and continue the post-hoc test?

Thank you very much.

June 13, 2021 at 4:51 am

Hi Jim, There are so many books covering maths/programming related to statistics/DS, but may be hardly any book to develop an intuitive understanding. Thanks to you for filling up that gap. After statistics, hypothesis-testing, regression, will it be possible for you to write such books on more topics in DS such as trees, deep-learning etc.

I recently started with reading your book on hypothesis testing (just finished the first chapter). I have a question w.r.t the fuel cost example (from first chapter), where a random sample of 25 families (with sample mean 330.6) is taken. To do the hypothesis testing here, we are taking a sampling distribution with a mean of 260. Then based on the p-value and significance level, we find whether to reject or accept the null hypothesis. The entire decision (to accept or reject the null hypothesis) is based on the sampling distribution about which i have the following questions : a) we are assuming that the sampling distribution is normally distributed. what if it has some other distribution, how can we find that ? b) We have assumed that the sampling distribution is normally distributed and then further assumed that its mean is 260 (as required for the hypothesis testing). But we need the standard deviation as well to define the normal distribution, can you please let me know how do we find the standard deviation for the sampling distribution ? Thanks.

April 24, 2021 at 2:25 pm

Maybe its the idea of “Innocent until proven guilty”? Your Null assume the person is not guilty, and your alternative assumes the person is guilty, only when you have enough evidence (finding statistical significance P0.05 you have failed to reject null hypothesis, null stands,implying the person is not guilty. Or, the person remain innocent.. Correct me if you think it’s wrong but this is the way I interpreted.

April 25, 2021 at 5:10 pm

I used the courtroom/trial analogy within this post. Read that for more details. I’d agree with your general take on the issue except when you have enough evidence you actually reject the null, which in the trial means the defendant is found guilty.

April 17, 2021 at 6:10 am

Can regression analysis be done using 5 companies variables for predicting working capital management and profitability positive/negative relationship?

Also, does null hypothesis rejecting means whatsoever is stated in null hypothesis that is false proved through regression analysis?

I have very less knowledge about regression analysis. Please help me, Sir. As I have my project report due on next week. Thanks in advance!

April 18, 2021 at 10:48 pm

Hi Ahmed, yes, regression analysis can be used for the scenario you describe as long as you have the required data.

For more about the null hypothesis in relation to regression analysis, read my post about regression coefficients and their p-values . I describe the null hypothesis in it.

January 26, 2021 at 7:32 pm

With regards to the legal example above. While your explanation makes sense when simplified to this statistical level, from a legal perspective it is not correct. The presumption of innocence means one does not need to be proven innocent. They are innocent. The onus of proof lies with proving they are guilty. So if you can’t prove someones guilt then in fact you must accept the null hypothesis that they are innocent. It’s not a statistical test so a little bit misleading using it an example, although I see why you would.

If it were a statistical test, then we would probably be rather paranoid that everyone is a murderer but they just haven’t been proven to be one yet.

Great article though, a nice simple and thoughtout explanation.

January 26, 2021 at 9:11 pm

It seems like you misread my post. The hypothesis testing/legal analogy is very strong both in making the case and in the result.

In hypothesis testing, the data have to show beyond a reasonable doubt that the alternative hypothesis is true. In a court case, the prosecutor has to present sufficient evidence to show beyond a reasonable doubt that the defendant is guilty.

In terms of the test/case results. When the evidence (data) is insufficient, you fail to reject the null hypothesis but you do not conclude that the data proves the null is true. In a legal case that has insufficient evidence, the jury finds the defendant to be “not guilty” but they do not say that s/he is proven innocent. To your point specifically, it is not accurate to say that “not guilty” is the same as “proven innocent.”

It’s a very strong parallel.

January 9, 2021 at 11:45 am

Just a question, in my research on hypotheses for an assignment, I am finding it difficult to find an exact definition for a hypothesis itself. I know the defintion, but I’m looking for a citable explanation, any ideas?

January 10, 2021 at 1:37 am

To be clear, do you need to come up with a statistical hypothesis? That’s one where you’ll use a particular statistical hypothesis test. If so, I’ll need to know more about what you’re studying, your variables, and the type of hypothesis test you plan to use.

There are also scientific hypotheses that you’ll state in your proposals, study papers, etc. Those are different from statistical hypotheses (although related). However, those are very study area specific and I don’t cover those types on this blog because this is a statistical blog. But, if it’s a statistical hypothesis for a hypothesis test, then let me know the information I mention above and I can help you out!

November 7, 2020 at 8:33 am

Hi, good read, I’m kind of a novice here, so I’m trying to write a research paper, and I’m trying to make a hypothesis. however looking at the literature, there are contradicting results.

researcher A found that there is relationship between X and Y

however, researcher B found that there is no relationship between X and Y

therefore, what is the null hypothesis between X and y? do we choose what we assumed to be correct for our study? or is is somehow related to the alternative hypothesis? I’m confused.

thank you very much for the help.

November 8, 2020 at 12:07 am

Hypotheses for a statistical test are different than a researcher’s hypothesis. When you’re constructing the statistical hypothesis, you don’t need to consider what other researchers have found. Instead, you construct them so that the test only produces statistically significant results (rejecting the null) when your data provides strong evidence. I talk about that process in this post.

Typically, researchers are hoping to establish that an effect or relationship exists. Consequently, the null and alternative hypotheses are typically the following:

Null: The effect or relationship doesn’t not exist. Alternative: The effect or relationship does exist.

However, if you’re hoping to prove that there is no effect or no relationship, you then need to flip those hypotheses and use a special test, such as an equivalences test.

So, there’s no need to consider what researchers have found but instead what you’re looking for. In most cases, you are looking for an effect/relationship, so you’d go with the hypotheses as I show them above.

I hope that helps!

October 22, 2020 at 6:13 pm

Great, deep detailed answer. Appreciated!

September 16, 2020 at 12:03 pm

Thank you for explaining it too clearly. I have the following situation with a Box Bohnken design of three levels and three factors for multiple responses. F-value for second order model is not significant (failing to reject null hypothesis, p-value > 0.05) but, lack of fit of the model is not significant. What can you suggest me about statistical analysis?

September 17, 2020 at 2:42 am

Are your first order effects significant?

You want the lack of fit to be nonsignificant. If it’s significant, that means the model doesn’t fit the data well. So, you’re good there! 🙂

September 14, 2020 at 5:18 pm

thank you for all the explicit explanation on the subject.

However, i still got a question about “accepting the null hypothesis”. from textbook, the p-value is the probability that a statistic would take a value that is as extreme as or more extreme than that actually observed.

so, that’s why when p<0.01 we reject the null hypothesis, because it's too rare (p0.05, i can understand that for most cases we cannot accept the null, for example, if p=0.5, it means that the probability to get a statistic from the distribution is 0.5, which is totally random.

But how about when the p is very close to 1, like p=0.95, or p=0.99999999, can’t we say that the probability that the statistic is not from this distribution is less than 0.05, | or in another way, the probability that the statistic is from the distribution is almost 1. can’t we accept the null in such circumstance?

September 11, 2020 at 12:14 pm

Wow! This is beautifully explained. “Lack of proof doesn’t represent proof that something doesn’t exist!”. This kinda, hit me with such force. Can I then, use the same analogy for many other things in life? LOL! 🙂

H0 = God does not exist; H1 = God does exist; WE fail to reject H0 as there is no evidence.

Thank you sir, this has answered many of my questions, statistically speaking! No pun intended with the above.

September 11, 2020 at 4:58 pm

Hi, LOL, I’m glad it had such meaning for you! I’ll leave the determination about the existence of god up to each person, but in general, yes, I think statistical thinking can be helpful when applied to real life. It is important to realize that lack of proof truly is not proof that something doesn’t exist. But, I also consider other statistical concepts, such as confounders and sampling methodology, to be useful keeping in mind when I’m considering everyday life stuff–even when I’m not statistically analyzing it. Those concepts are generally helpful when trying to figure out what is going on in your life! Are there other alternative explanations? Is what you’re perceiving likely to be biased by something that’s affecting the “data” you can observe? Am I drawing a conclusion based on a large or small sample? How strong is the evidence?

A lot of those concepts are great considerations even when you’re just informally assessing and draw conclusions about things happening in your daily life.

August 13, 2020 at 12:04 am

Dear Jim, thanks for clarifying. absolutely, now it makes sense. the topic is murky but it is good to have your guidance, and be clear. I have not come across an instructor as clear in explaining as you do. Appreciate your direction. Thanks a lot, Geetanjali

August 15, 2020 at 3:48 pm

Hi Geetanjali,

I’m glad my website is helpful! That makes my day hearing that. Thanks so much for writing!

August 12, 2020 at 9:37 am

Hi Jim. I am doing data analyis for my masters thesis and my hypothesis testings were insignificant. And I am ok with that. But there is something bothering me. It is the low reliabilities of the 4-Items sub-scales (.55, .68, .75), though the overall alpha is good (.85). I just wonder if it is affecting my hypothesis testings.

August 11, 2020 at 9:23 pm

Thank you sir for replying, yes sir we it’s a RCT study.. where we did within and between the groups analysis and found p>0.05 in between the groups using Mann Whitney U test. So in such cases if the results comes like this we need to Mention that we failed reject the null hypothesis? Is that correct? Whether it tells that the study is inefficient as we couldn’t accept the alternative hypothesis. Thanks is advance.

August 11, 2020 at 9:43 pm

Hi Saumya, ah, this becomes clearer. When ask statistical questions, please be sure to include all relevant information because the details are extremely important. I didn’t know it was an RCT with a treatment and control group. Yes, given that your p-value is greater than your significance level, you fail to reject the null hypothesis. The results are not significant. The experiment provides insufficient evidence to conclude that the outcome in the treatment group is different than the control group.

By the way, you never accept the alternative hypothesis (or the null). The two options are to either reject the null or fail to reject the null. In your case, you fail to reject the null hypothesis.

I hope this helps!

August 11, 2020 at 9:41 am

Sir, p value is0.05, by which we interpret that both the groups are equally effective. In this case I had to reject the alternative hypothesis/ failed to reject null hypothessis.

August 11, 2020 at 12:37 am

sir, within the group analysis the p value for both the groups is significant (p0.05, by which we interpret that though both the treatments are effective, there in no difference between the efficacy of one over the other.. in other words.. no intervention is superior and both are equally effective.

August 11, 2020 at 2:45 pm

Thanks for the additional details. If I understand correctly, there were separate analyses before that determined each treatment had a statistically significance effect. However, when you compare the two treatments, there difference between them is not statistically significant.

If that’s the case, the interpretation is fairly straightforward. You have evidence that suggests that both treatments are effective. However, you don’t have evidence to conclude that one is better than the other.

August 10, 2020 at 9:26 am

Hi thank you for a wonderful explanation. I have a doubt: My Null hypothesis says: no significant difference between the effect fo A and B treatment Alternative hypothesis: there will be significant difference between the effect of A and B treatment. and my results show that i fail to reject null hypothesis.. Both the treatments were effective, but not significant difference.. how do I interpret this?

August 10, 2020 at 1:32 pm

First, I need to ask you a question. If your p-value is not significant, and so you fail to reject the null, why do you say that the treatment is effective? I can answer you question better after knowing the reason you say that. Thanks!

August 9, 2020 at 9:40 am

Dear Jim, thanks for making stats much more understandable and answering all question so painstakingly. I understand the following on p value and null. If our sample yields a p value of .01, it means that that there is a 1% probability that our kind of sample exists in the population. that is a rare event. So why shouldn’t we accept the HO as the probability of our event was v rare. Pls can you correct me. Thanks, G

August 10, 2020 at 1:53 pm

That’s a great question! They key thing to remember is that p-values are a conditional probability. P-value calculations assume that the null hypothesis is true. So, a p-value of 0.01 indicates that there is a 1% probability of observing your sample results, or more extreme, *IF* the null hypothesis is true.

The kicker is that we don’t whether the null is true or not. But, using this process does limit the likelihood of a false positive to your significance level (alpha). But, we don’t know whether the null is true and you had an unusual sample or whether the null is false. Usually, with a p-value of 0.01, we’d reject the null and conclude it is false.

I hope that answered your question. This topic can be murky and I wasn’t quite clear which part you needed clarification.

August 4, 2020 at 11:16 pm

Thank you for the wonderful explanation. However, I was just curious to know that what if in a particular test, we get a p-value less than the level of significance, leading to evidence against null hypothesis. Is there any possibility that our interpretation of population effect might be wrong due to randomness of samples? Also, how do we conclude whether the evidence is enough for our alternate hypothesis?

August 4, 2020 at 11:55 pm

Hi Abhilash,

Yes, unfortunately, when you’re working with samples, there’s always the possibility that random chance will cause your sample to not represent the population. For information about these errors, read my post about the types of errors in hypothesis testing .

In hypothesis testing, you determine whether your evidence is strong enough to reject the null. You don’t accept the alternative hypothesis. I cover that in my post about interpreting p-values .

August 1, 2020 at 3:50 pm

Hi, I am trying to interpret this phenomenon after my research. The null hypothesis states that “The use of combined drugs A and B does not lower blood pressure when compared to if drug A or B is used singularly”

The alternate hypothesis states: The use of combined drugs A and B lower blood pressure compared to if drug A or B is used singularly.

At the end of the study, majority of the people did not actually combine drugs A and B, rather indicated they either used drug A or drug B but not a combination. I am finding it very difficult to explain this outcome more so that it is a descriptive research. Please how do I go about this? Thanks a lot

June 22, 2020 at 10:01 am

What confuses me is how we set/determine the null hypothesis? For example stating that two sets of data are either no different or have no relationship will give completely different outcomes, so which is correct? Is the null that they are different or the same?

June 22, 2020 at 2:16 pm

Typically, the null states there is no effect/no relationship. That’s true for 99% of hypothesis tests. However, there are some equivalence tests where you are trying to prove that the groups are equal. In that case, the null hypothesis states that groups are not equal.

The null hypothesis is typically what you *don’t* want to find. You have to work hard, design a good experiment, collect good data, and end up with sufficient evidence to favor the alternative hypothesis. Usually in an experiment you want to find an effect. So, usually the null states there is no effect and you have get good evidence to reject that notion.

However, there are a few tests where you actually want to prove something is equal, so you need the null to state that they’re not equal in those cases and then do all the hard work and gather good data to suggest that they are equal. Basically, set up the hypothesis so it takes a good experiment and solid evidence to be able to reject the null and favor the hypothesis that you’re hoping is true.

June 5, 2020 at 11:54 am

Thank you for the explanation. I have one question that. If Null hypothesis is failed to reject than is possible to interpret the analysis further?

June 5, 2020 at 7:36 pm

Hi Mottakin,

Typically, if your result is that you fail to reject the null hypothesis there’s not much further interpretation. You don’t want to be in a situation where you’re endlessly trying new things on a quest for obtaining significant results. That’s data mining.

May 25, 2020 at 7:55 am

I hope all is well. I am enjoying your blog. I am not a statistician, however, I use statistical formulae to provide insight on the direction in which data is going. I have used both the regression analysis and a T-Test. I know that both use a null hypothesis and an alternative hypothesis. Could you please clarity the difference between a regression analysis and a T-Test? Are there conditions where one is a better option than the other?

May 26, 2020 at 9:18 pm

t-Tests compare the means of one or two groups. Regression analysis typically describes the relationships between a set of independent variables and the dependent variables. Interestingly, you can actually use regression analysis to perform a t-test. However, that would be overkill. If you just want to compare the means of one or two groups, use a t-test. Read my post about performing t-tests in Excel to see what they can do. If you have a more complex model than just comparing one or two means, regression might be the way to go. Read my post about when to use regression analysis .

May 12, 2020 at 5:45 pm

This article is really enlightening but there is still some darkness looming around. I see that low p-values mean strong evidence against null hypothesis and finding such a sample is highly unlikely when null hypothesis is true. So , is it OK to say that when p-value is 0.01 , it was very unlikely to have found such a sample but we still found it and hence finding such a sample has not occurred just by chance which leads towards rejection of null hypothesis.

May 12, 2020 at 11:16 pm

That’s mostly correct. I wouldn’t say, “has not occurred by chance.” So, when you get a very low p-value it does mean that you are unlikely to obtain that sample if the null is true. However, once you obtain that result, you don’t know for sure which of the two occurred:

• The effect exists in the population.
• Random chance gave you an unusual sample (i.e., Type I error).

You really don’t know for sure. However, by the decision making results you set about the strength of evidence required to reject the null, you conclude that the effect exists. Just always be aware that it could be a false positive.

That’s all a long way of saying that your sample was unlikely to occur by chance if the null is true.

April 29, 2020 at 11:59 am

Why do we consult the statistical tables to find out the critical values of our test statistics?

April 30, 2020 at 5:05 pm

Statistical tables started back in the “olden days” when computers didn’t exist. You’d calculate the test statistic value for your sample. Then, you’d look in the appropriate table and using the degrees of freedom for your design and find the critical values for the test statistic. If the value of your test statistics exceeded the critical value, your results were statistically significant.

With powerful and readily available computers, researchers could analyze their data and calculate the p-values and compare them directly to the significance level.

I hope that answers your question!

April 15, 2020 at 10:12 am

If we are not able to reject the null hypothesis. What could be the solution?

April 16, 2020 at 11:13 pm

Hi Shazzad,

The first thing to recognize is that failing to reject the null hypothesis might not be an error. If the null hypothesis is false, then the correct outcome is failing to reject the null.

However, if the null hypothesis is false and you fail to reject, it is a type II error, or a false negative. Read my post about types of errors in hypothesis tests for more information.

This type of error can occur for a variety of reasons, including the following:

• Fluky sample. When working with random samples, random error can cause anomalous results purely by chance.
• Sample is too small. Perhaps the sample was too small, which means the test didn’t have enough statistical power to detect the difference.
• Problematic data or sampling methodology. There could be a problem with how you collected the data or your sampling methodology.

There are various other possibilities, but those are several common problems.

April 14, 2020 at 12:19 pm

Thank you so much for this article! I am taking my first Statistics class in college and I have one question about this.

I understand that the default position is that the null is correct, and you explained that (just like a court case), the sample evidence must EXCEED the “evidentiary standard” (which is the significance level) to conclude that an effect/relationship exists. And, if an effect/relationship exists, that means that it’s the alternative hypothesis that “wins” (not sure if that’s the correct way of wording it, but I’m trying to make this as simple as possible in my head!).

But what I don’t understand is that if the P-value is GREATER than the significance value, we fail to reject the null….because shouldn’t a higher P-value, mean that our sample evidence EXCEEDS the evidentiary standard (aka the significance level), and therefore an effect/relationship exists? In my mind it would make more sense to reject the null, because our P-value is higher and therefore we have enough evidence to reject the null.

I hope I worded this in a way that makes sense. Thank you in advance!

April 14, 2020 at 10:42 pm

That’s a great question. The key thing to remember is that higher p-values correspond to weaker evidence against the null hypothesis. A high p-value indicates that your sample is likely (high probability = high p-value) if the null hypothesis is true. Conversely, low p-values represent stronger evidence against the null. You were unlikely (low probability = low p-value) to have collect a sample with the measured characteristics if the null is true.

So, there is negative correlation between p-values and strength of evidence against the null hypothesis. Low p-values indicate stronger evidence. Higher p-value represent weaker evidence.

In a nutshell, you reject the null hypothesis with a low p-value because it indicates your sample data are unusual if the null is true. When it’s unusual enough, you reject the null.

March 5, 2020 at 11:10 am

There is something I am confused about. If our significance level is .05 and our resulting p-value is .02 (thus the strength of our evidence is strong enough to reject the null hypothesis), do we state that we reject the null hypothesis with 95% confidence or 98% confidence?

My guess is our confidence level is 95% since or alpha was .05. But if the strength of our evidence is 98%, why wouldn’t we use that as our stated confidence in our results?

March 5, 2020 at 4:19 pm

Hi Michael,

You’d state that you can reject the null at a significance level of 5% or conversely at the 95% confidence level. A key reason is to avoid cherry picking your results. In other words, you don’t want to choose the significance level based on your results.

Consequently, set the significance level/confidence level before performing your analysis. Then, use those preset levels to determine statistical significance. I always recommend including the exact p-value when you report on statistical significance. Exact p-values do provide information about the strength of evidence against the null.

March 5, 2020 at 9:58 am

Thank you for sharing this knowledge , it is very appropriate in explaining some observations in the study of forest biodiversity.

March 4, 2020 at 2:01 am

Thank you so much. This provides for my research

March 3, 2020 at 7:28 pm

If one couples this with what they call estimated monetary value of risk in risk management, one can take better decisions.

March 3, 2020 at 3:12 pm

Thank you for providing this clear insight.

March 3, 2020 at 3:29 am

Nice article Jim. The risk of such failure obviously reduces when a lower significance level is specified.One benefits most by reading this article in conjunction with your other article “Understanding Significance Levels in Statistics”.

March 3, 2020 at 2:43 am

That’s fine. My question is why doesn’t the numerical value of type 1 error coincide with the significance level in the backdrop that the type 1 error and the significance level are both the same ? I hope you got my question.

March 3, 2020 at 3:30 am

Hi, they are equal. As I indicated, the significance level equals the type I error rate.

March 3, 2020 at 1:27 am

Kindly elighten me on one confusion. We set out our significance level before setting our hypothesis. When we calculate the type 1 error, which happens to be a significance level, the numerical value doesn’t equals (either undermining value comes out or an exceeding value comescout ) our significance level that was preassigned. Why is this so ?

March 3, 2020 at 2:24 am

Hi Ratnadeep,

You’re correct. The significance level (alpha) is the same as the type I error rate. However, you compare the p-value to the significance level. It’s the p-value that can be greater than or less than the significance level.

The significance level is the evidentiary standard. How strong does the evidence in your sample need to be before you can reject the null? The p-value indicates the strength of the evidence that is present in your sample. By comparing the p-value to the significance level, you’re comparing the actual strength of the sample evidence to the evidentiary standard to determine whether your sample evidence is strong enough to conclude that the effect exists in the population.

I write about this in my post about the understanding significance levels . I think that will help answer your questions!

Comments and Questions Cancel reply

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

17 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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

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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 ).

You Can’t Prove a Negative myth

Proving Negatives

The saying “you can’t prove a negative” isn’t accurate. Proving negatives is a foundational aspect of  logic (ex. the law of contradiction). [1] [2] [3] [4]

Furthermore, if you define “proof” as something that only requires us to show that something is very likely, then you can prove a negative this way as well.

Below I’ll cover the different ways to prove negatives, including the two just mentioned, and I will cover why the statement “you can’t prove a negative” has weight to it despite this.

What Does Proving a Negative Mean?

So first, “what does proving a negative mean?”

It means proving something isn’t true. For example, “proving Santa Claus doesn’t exist.”

If Santa did exist, you could find evidence and prove it, but because [spoiler] it is very likely he doesn’t exist, you can’t find evidence to prove he doesn’t with certainty, you can only find evidence that suggests he doesn’t (you can only find “evidence of absence”).

The Absence of Evidence and the Evidence of Absence – What Do People Mean When they Say “You Can’t Prove a Negative”?

In general, and putting aside those who misunderstand the concept, when people use the phrase “you can’t prove a negative” they mean: you can’t prove negatives with certainty based on the absence of evidence alone (the absence of evidence is not necessarily the evidence of absence).

For example, having no proof of Bigfoot doesn’t prove that he isn’t real with certainty, it just means you can’t find evidence that he is real.

Likewise, it is hard to provide proof that a giant flying invisible unicorn doesn’t exist… because there is no evidence of such a thing and thus our best evidence is an absolute lack of evidence.

We can only “prove” that which there is no evidence for with a high degree of probability (by considering the lack of evidence and some rules of logic).

However, while the above is true (one reason the “you can’t prove a negative” saying has weight to it), the reality is we can’t prove positives very well either.

Most proofs (positive or negative) rely on inductive evidence , and induction necessarily always produces probable conclusions and not certain ones.

So for example, if we had Santa on tape admitting he was Santa… it would still only be very strong evidence (it wouldn’t prove he Santa was real with certainty; our senses could be tricking us, the video could be fake, the person may be lying, we might be in the Matrix, etc).

In other words, we could argue that proving both positives and negatives rely on likelihood and not certainty.

TIP : Learn about how induction and deduction work . The certain proofs are deductive, the likely proofs are inductive.

Absence of evidence and the evidence of absence : Absence of evidence is an ambiguous term. If it is absence from ignorance, in that no one has ever carefully studied the matter, then it means next to nothing. If it is absence despite careful empirical study done in-line with the scientific method, then the absence of evidence itself can be considered a type of scientific evidence. If we inspect the room over and over and there is never any mice in the room, we can conclude with a high degree of certainty from the absence of mice that the room is not infested with mice. Here absence of evidence (or “the evidence of absence despite our looking for it” more specifically) is a type of evidence. If we keep checking and don’t see evidence of Santa, we can be highly confident that there is no Santa. See “ Evidence of absence .”

An Example of Proving a Negative With Likelihoods and the Evidence of Absence

As alluded to above, one way to “prove” a negative with a high degree of certainty is to show enough evidence of absence.

Consider the following argument:

• If to “ prove ” something we simply have to provide sufficient evidence that a proposition (statement or claim) is very likely true .
• Then, to prove a negative, we only have to show that it is very likely the case and we don’t have to show it is true with absolute certainty .

Under those conditions, we DO NOT have to observe empirically that which cannot be observed (for example, we don’t have to see a Unicorn not existing to know it doesn’t exist, we just have to show compelling evidence of its non-existence).

Thus, proving a negative in this sense can be accomplished by providing evidence of absence (not an argument from ignorance , but scientific evidence of absence gathered from scientific research that shows absence).

For example, a strong argument that proves that it is very likely Unicorns don’t exist on earth involves showing that there is no evidence of Unicorns existing on earth (no fossils, no eye witness accounts, no hoofprints, nothing).

If we did a serious scientific inquiry, searching for Unicorn fossils, and turned up nothing, it would be a type of evidence for the non-existence of Unicorns. If no one could show scientific data pointing toward unicorns to combat this, then at a point it would become a good theory and we could put forth a scientific theory, based on empirical data, that says “Unicorns don’t exist on earth.”

At that point, the burden of proof would be on those who believe in Unicorns to prove that Unicorns do in fact exist (the burden would be on them to prove the theory of non-existent Unicorns false by providing a better theory).

In other words, if we accept that a proof can have a degree of uncertainty, we can argue that it is possible to use the evidence of absence to prove negatives.

However, since we didn’t prove the non-existence of unicorns with certainty, below we deal with the law of contradiction and using double negatives to provide more certain proofs.

TIP : Science can’t actually prove anything with 100% certainty. Essentially, “all we know for sure is that we know nothing for sure.” This is because all testing of the outside world involves inductive reasoning (comparing specific observations to other specific observations), and inductive reasoning is by its nature uncertain (for example the statement “I see a horse there, therefore horses exist on earth” could be wrong if your eyes aren’t working AKA if your measuring device is off, if you are in a simulation, if that isn’t actually a horse, or if this isn’t actually earth, etc…. in short, it is a more compelling argument than the unicorn argument, but still something we can poke holes in). Meanwhile, logically certain truths are generally pure analytic a priori (they are generally tautologically redundant and necessarily true facts; for example, “since A is A”  therefore “A is not B.”) With those logical truths we have the positive side “A is A” and the negative side “A is not B.”

Does this prove God does or doesn’t exist? Proving the existence of God (or the non-existence) is loosely related to this line of reasoning, but it is sort of outside of the sphere of what we are talking about here. If one claims, “all that is is, but God exists outside of that” then the argument for God becomes ontological, theological, metaphysic, and faith-based. Faith-based metaphysical arguments don’t require scientific empirical evidence… unless they try to posit something that can be debunked by empirical science (in that case, arguments for faith instead of reason tend to be logically “weak,” in that they lack supporting evidence).

An Introduction to Proving Negatives With Necessary Logical Truths

Above we “proved” an argument using likelihoods (not certainty). The idea was to show that using evidence to prove a positive and the evidence of absence to prove a negative were both valid.

With that said, we can actually prove some negatives with certainty (for example, necessary logical truths such as “nothing can both be and not be” and double negatives like “I do not not exist”).

Here are some examples of proving negatives with logical truths:

• The Law of Contradiction itself is a negative : “Nothing can be A and not A.” Ex. Ted can’t be in Room A and not in Room A (and therefore, if Ted is in Room A, then Ted is not in Room B). We are using a positive to prove a negative with the law of contradiction, but we are proving a negative. This is a rule used in deductive reasoning and is a necessarily true logical rule.
• The Modus Tollens also proves a negative : “If P, then Q. Not Q. Therefore, not P.” Ex. “If the cake is made with sugar, then the cake is sweet. The cake is not sweet. Therefore, the cake is not made with sugar.” Not every argument of this structure is true, but we are proving a negative… as we are simply trying to prove “not P.” This is also a logical rule that relates to deductive reasoning. [5]
• Proving a negative with certainty using double negatives : Any true positive statement can be made negative and proved that way. Ex. I do not not exist; or Every A is A, nothing can be A and not A, everything is either A or not A, therefore A is not not A. These prove a negative with certainty, but are somewhat redundant (rephrasing “A is A” as “A is not not A” is tautological ).
• Proving Impossibility . In mathematics there are different ways to prove a problem can’t be solved. For example, because π is non-algebraic, and only a subset of the algebraic numbers can be constructed by compass and straightedge, you can’t square a circle with a compass and straightedge. Here you could argue that we are again first proving a positive (that π is non-algebraic, that only a subset of the algebraic numbers can be constructed by compass and straightedge, etc)… but ultimately it is another example of providing negatives despite this.

How to Using the Above Logical Arguments To Structure an Argument that Attempts to Prove a Negative

As noted above, the law of contradiction states that a proposition (statement) cannot be both true and not true (unlike the positive rule of identity that says “whatever is, is.”)

That “law” is part of three laws that comprise the “laws of thought.”

Those laws are:

• The Law of Identity : Whatever is, is; or, in a more precise form, Every A is A. Ex. Whatever is true about Santa is true about Santa.
• The Law of Contradiction : Nothing can both be and not be; Nothing can be A and not A. Ex. Santa cannot be real and not real at the same time.
• The Law of Excluded Middle : Everything must either be or not be; Everything is either A or not A. Ex. Santa must be real or not real.

In other words, Santa is either real or not real, there is no in-between.

With that covered, we can now apply the following Modus Tollens style logic on top of the idea that “Santa is either real or not”:

• If Santa was real there would likely be some evidence of Santa (not certain).
• There is no evidence of Santa that we have found (this has more weight if we truly look for the evidence).
• Therefore we can reasonably infer that Santa is very likely not real (a likely truth inferred using inductive reasoning based on the absence of evidence).

Here we could try to prove that it is impossible for Santa to be real, but that is aside from the point. The point is, once we have to prove something empirically, once we start dealing with the evidence of absence, we introduce likelihoods.

Ultimately the Argument For Proving Negatives or Not Depends on How We Define “Prove”

In mathematics and logic, when we replace empirical evidence for numbers and symbols, we can prove negatives all day.

However, when we go to prove negatives in the material world using empirical evidence, we have to deal with evidence of absence, and thus end up dealing with likelihoods and not certainties.

Therefore, in many ways, the argument that “you can’t prove a negative” relies on how we define the term “prove.”

If to prove something is to prove absolute certainty, then only tautological forms of deductive logical truths, like A is A, are valid. Meanwhile, induction is invalid and thus even the empirical evidence we gather to prove positives is called into question (because “what if we can’t trust our sense”).

If we on the other hand can consider overwhelming evidence that draws a highly certain conclusion as proof until better evidence comes along, then we can prove negatives.

However, if we say, yes we can consider overwhelming evidence, but only the evidence of presence and not the evidence of absence. Then, well, we get the old “you can’t prove a negative line.”

With all that said, since there are arguments for providing a negative with the evidence of absence, and since there are aspects of deductive logic that involve providing negatives, I would still put fort the idea that the saying “we can’t prove a negative” is ultimately misleading if not flat out wrong.

Summary of the Different Ways to Prove a Negative

• The Law of Contradiction proves a negative with certainty : Nothing can both be and not be; Nothing can be A and not A.
• The Modus Tollens also proves a negative : “If P, then Q. Not Q. Therefore, not P.”
• We Can Use Inductive Reasoning to Provide a Likely Proof : We can show evidence of absence as proof of likelihood.
• We Can Also use Double Negation : Simply converting a positive statement into a double negative.
• We can generally use a mix of all the above.

TIP : For more reading, see:  “You Can Prove a Negative ” Steven D. Hales Think Vol. 10, Summer 2005 pp. 109-112 .

• “You Can Prove a Negative ” Steven D. Hales Think Vol. 10, Summer 2005 pp. 109-112
• Evidence of absence
• Argument from ignorance
• Modus tollens

While it is true that the absence of evidence isn’t the evidence of absence, the blanket statement “we can’t prove a negative” is arguably is misleading if not fully incorrect (especially when we are talking about deductive logic like the law of contradiction).

Thomas DeMichele is the content creator behind ObamaCareFacts.com, FactMyth.com, CryptocurrencyFacts.com, and other DogMediaSolutions.com and Massive Dog properties. He also contributes to MakerDAO and other cryptocurrency-based projects. Tom's focus in all...

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Wanda Pieczynski Did not vote.

An interesting article. The author clearly put a lot of effort into it. I will have to re-read the article more carefully when I have some time.

Possibly I am not the only person who commented on this, but “elude” is not the correct word to use in the section with the heading “An Example of Proving a Negative With Likelihoods and the Evidence of Absence.” I believe the correct word would be “allude.”

Thomas DeMichele The Author Did not vote.

Good catch, thank you.

Charles Peterson Supports this as a Fact.

Sure used a lot of fancy words to get to one important point: it all depends on how we define “prove”. Moreover, it also depends on consistently defining “prove” and “proof”. My experience suggests most people are inherently hypocritical, demanding lesser proof for concepts in which they believe than they demand of concepts in which they don’t believe. But you knew that, or you wouldn’t have repeatedly used examples widely considered fantastical. Consider instead the fact that there were four centuries and hundreds or possibly thousands of first-hand accounts of “supernaturally” giant (squids) before scientists stopped scoffing at the legends and lunacy. That’s a nearly unfathomable level of ignorance from a group of people characterized by their education and enthusiasm for discovery, methinks. This supports the thought that, “For those who believe, no evidence is necessary. For those who do not, no evidence is adequate.”. I mean, has anyone here ever CHANGED a mind with any of this garbledy gook?!

I appreciate the comment. I’ll maybe consider moving that up top so I’m not burying the lead here.

And, I have no idea if anyone has ever changed their mind after hearing an argument. But I do think minds are changed over time by people with open minds based on hearing enough arguments.

Paul Knox Did not vote.

Suggestion: consider a new version of this article in which the propositions are more rigorous, excluding extraneous factors that compromise validity.

Also consider expanding by substituting probability for likelihood and exploring it as the key concept (along with inference) in statistics. And by discussing the impact of quantum physics and uncertainty on the subject.

Uncertainty derives from the knowledge that every particle is in constant motion, the sum of particle effects on each other is infinite, and therefore there are limits to the capacity of empirical methods to generate the knowledge needed to establish a set of beliefs. Because we swim in a sea of uncertainty, we need guidance to set our course. Inductive methods aimed at establishing probability are popular because they provide the tools for defining risk, which reduces (but doesn’t eliminate) uncertainty.

Also consider that the question asked exists in more than one dimension and on more than one plane.

Time is a crucial dimension. Evidence of existence fades in and fades out. Concepts are born, become salient, then obsolete. Do they cease to exist or merely cease to be relevant? What happens to validating methods over time? Does probability always become harder to define over time, and see its predictive value diminished?

Representation is a layer in propositions that poses its own challenges to validity. The way human beings, other life forms and their environment are represented in formulating propositions can affect their validity and usefulness. Variations across cultures may be significant. The way in which a unicorn is represented can influence validity criteria, and these may vary among communities.

Trust is important to some commenters here when the concepts of truth, evidence and validity are under discussion. Some are partial to induction and probability because they believe they can unlock all the secrets of he actually existing universe. Others trust in deities – the unabashedly metaphysical – as either a substitute or a complement. This trust bridges the gap between empirical evidence and knowledge or belief.

Thanks for the stimulation that helps produce these thoughts.

Thank your or your feedback!

Paul Snyder Supports this as a Fact.

It all depends on how you define proof. Applying formal logic, if you can demonstrate that a “negative” is sufficiently unlikely you have proven the fact. We could quibble over the domain and tautology of formal logic, but examination of one fact demonstrates the limits of proof. To be able to statistically analyze and prove the possibility of an event occurring you must be able to observe the event when it happens. If the event is outside human perception, even with instrumentation, you can never count to see if the event happens over a given period. Therefore, you cannot say anything objective about the event at all. The event may be true or may be false, more importantly you cannot say it is more likely to be true or false. In this sense you cannot prove the negative, or the positive, of that which is beyond human perception. You cannot even say that it is likely or unlikely that anything exists beyond human perception.

Trista Did not vote.

I disagree with this argument based on many things. You can prove A is A and A is B. Ex. The saying you can’t be in two places at once is untrue. By straddling a state line you are in two different states, and by standing in a door way you are both in and out of a doorway. Also If P is Q then whatever follows must always be true is as you have previously stated inaccurate. You can have sugar in something and it not be sweet and you can have no sugar in something and the object be sweet if using literal interpretation. As for the final argument of double negatives is complex. You can prove that Santa is both real and not real for example Santa used to exist so in essence he is real, however he has long passed away and the modern stories of him are now mixed with myth and legend that his legend isn’t real therefore he also doesn’t exist. Also as you have previously stated it would be based on likelihood and probability. All of the proof of how that negates your statements does have high likelihood and probability as well. All of this as you’ve stated also depends on perspective. You can say that you can’t prove the existence so therefore evidence of absence proves the lack of existence. Who’s to say unicorns don’t exist they could just be fantastical ideas of what people thought rhinoceros were having never seen one. That disproves the idea unicorns don’t exist, but instead is a change in perspective on what a unicorn is. Who’s to say God doesn’t exist based on the idea that God isn’t imaginable based on our limited understanding and perspective maybe God is life or maybe matter and maybe energy and maybe antimatter and maybe all of it and maybe because our perspectives might still be too limited that we can’t fathom that there might be something else or something more that entails what God is. None of these previous arguments is based on faith alone or lack of logic and probability just on the idea that perspective does change an idea of what the existence of God might be. Now for the final point is the idea that you can prove guilt, but not innocence is reasonable. Once someone is found guilty unless there’s evidence proving another of guilt when no conspiracy is involved then unless another is proven guilty it’s almost impossible to prove reasonably that the person isn’t guilty. It’s more difficult to prove innocence than it is to prove guilt which is why innocent until proven guilty is such an important ideal to live by. Which has a lot to do with this statement and the importance of it.

Trista Supports this as a Fact.

Furthermore it is important to distinguish the difference in wording and therefore meaning to me. Absence of evidence means that no evidence has been produced which means evidence could still be produced. Evidence of absence is proof and evidence of the lack of something. Which is basically proving a negative, very different meanings.

Guy Griffith Did not vote.

I believe in simple terms of reasoning and proof, indeed one has to establish and have agreement on what is proof.. If proof is accepting reasonable arguments for a position, then one can prove anything.

If proof requires evidence, then the presence of evidence becomes proof. In that case one3 cannot prove some things.

The absence of evidence does not mean the evidence is absent, only that it has not been produced. In that regards, it is still satisfactory to conclude that the negative has not been proven so far. Science has had positions that certain things did not exist because the equipment or technology at the time did not permit the evidence to be obtained. With technological developments, Science has had to be rewritten because things that were not supposed to be there are proven to be there. Hence the production of evidence has confirmed the existence or reality of things. Quantum Physics or Quantum Mechanics has made a reconsideration of many previous Scientific “Truths”. The meaning of all this is that one cannot prove scientifically that something does not exist unless one has the capability to test the non-existence of that thing to the limit. It is certainly more rational to conclude that one cannot prove a negative, than to argue that one can by modifying the meaning of proof! Certainly the thought “There is no God” is an awesome thing to prove!

Luculent Morningstar Supports this as a Fact.

Modus Tollens Fallacy: Proposed: “If P, then Q. Not Q. Therefore, not P.” Fallacy Proof: If you behead the King, then he will die. If the King does not die. Therefore, he will not be beheaded.

Proposed: “If P, then Q. Not P. Therefore, not Q.” Fallacy Proof: If you behead the King, then he will die. You don’t behead the King. Therefore, the King won’t die.

This made me laugh. Lots of big words and references to the mathematics of logic… which I have taken and use professionally on a daily basis. You lose all credibility with; double negatives like “I don’t not not exist”… this is a triple negative, genius. How about; “since A is A” therefore “A is not B.” This is in correct logic and proves nothing. If B=A, which you did not state that is was not, then “A is not B” is a false statement. Or; “if Ted is in Room A, then Ted is not in Room B”, “if Ted is in Room A” is proving a positive. Now if you said “Ted is not in room D”, that is a negative… does that mean he is in room A? Modus Tollens is a fallacy of propositional logic by denying the antecedent. This ‘Denying the Antecedent’ is non-validating, which means that not every argument of that form is valid. This doesn’t mean that every argument that denies the antecedent is invalid; rather, it means that some arguments of that form are invalid. Since proof exists is in the real world, how many rooms are there out there? A million? A billion? Thus proving this logic flawed. For the rest of these examples, it is just as misleading… “If P…“; P a positive, “induction doesn’t prove negatives”; this as an argument to prove negatives? This one made me laugh the hardest. “Proving a negative with certainty using double negatives”… as you, yourself pointed out that double negatives cancel each other out (as they do in mathematics) with this example; if “A is A” as “A is not not A.” Thus “A is A” = “A is not not A” then this statement is actually “Proving a negative with certainty using a positive”. This is too funny! Or better yet (or worse in this case), the comment; “<-This is how science works"… maybe, but not logic… which is the argument of topic. I am not sure you understand the OR and AND of logic correctly. The positive aspect of an argument is an OR. If I can show empirical evidence that something exists, (Unicorn Bones, DNA, baby unicorns) then I have proven my case. It does not matter how many people have not found bones, have not found babies, or have not found DNA, a single proof is all that is needed. A negative is an AND. To prove a negative you have to cross off everything in the list to prove it. Thus if I said “there is no such thing as Unicorns” I would have to have looked everywhere and every when, to prove this is the case. You cannot prove a negative, period. Take this sentence; "Thus, to prove a negative, we only have to show that it is very likely the case." Not true… implication of proof, is still not proof. Given the example above, you would have to examine all planets, in all solar systems, for all previous existing eras, since the beginning of time, to prove Unicorns do not now exist and have never existed. This limited scope of proof, proves only your limited scope of true understanding. Since this is not possible, proving that they do not exist and that have never existed, is not possible. Again, implication of proof, is still not proof. Just because you have looked in one microscopic section of the universe, within a miniscule sliver of time and found no evidence, this does not prove Unicorns do not, or have not ever existed. “At that point, the burden of proof would be on those who believe in Unicorns to prove that Unicorns do in fact exist.” Really? At what point? When you haven’t yet proved anything? “I looked in my closet and did not see any, so it is up to you to prove they do exist.” So the argument for “proving a negative” is to have the opposing side of the argument prove a positive? Hilarious. Proof is obtained through empirical evidence. This does not mean lack of empirical evidence proves it does not exist. Modus Tollens Fallacy. Talk about “an argument from ignorance”, I laughed all the way through this convolution of dribble.

Interesting counterpoints. I’ll have to read this over carefully.

UPDATE: I read this over. It was a rather insulting (at times going from academic to ad hominem) and insanely long paragraph… but, that aside, it was also full of interesting counter arguments and a few very valid points (like the one about double negatives… my example was incorrect). I think this all boils down to a debate between “how do we define proof” regarding most of what I’m talking about, and to some degree semantics in other cases. For example with the Unicorn, it is “how do we define proof” (basically I say we can provide a very likely reasonable proof, a very compelling inductive argument; and you say “that is not proof,” only certain empirical evidence is proof…) and meanwhile for example with double negation we seem to be in an argument of semantics (you say basically not-not-P doesn’t cut it, I say but it does). Anyway, all that aside, I appreciate the feedback as 1. it helped me to make my argument stronger, and 2. it points out where I should clearly state the opposing view (as I think many people will ultimately agree with your stance, and under certain criteria I could make an argument for it as well).

Instead of: If you behead the King, then he will die. If the King does not die. Therefore, he will not be beheaded.

Try: If you behead the King, then he will die. If the King is not dead. Therefore, he was not be beheaded.

Same structure, a different wording, point being we are trying to prove the King was not beheaded.

Mike Did not vote.

I believe that you are arguing Most of this from a mindset of needing to know (or needing to be on one side of the line). Example: I walked my child to the park. The sign on the park entrance says “Park is poisonous. Do not enter.” I need to decide whether or not I enter the park, and my mind tells me to decide whether or not the sign is true. The reality is that the only decision that has to be made is whether or not I enter the park, not whether or not I believe it. Undecided is a place we should all frequent most of the time and for most things. I hope that I’m not being confusing.

I’ll end in a possibly more confusing way by using two wisdoms: 1) most people live life thinking that there is a line drawn on every issue and that we must be on one side or the other for the line is an absence of ground/existence. The truth is that the line isn’t thin but It is a wide chasm that should be trekked through carefully before taking a stance on any side. 2) we are all ignorant of most things.

imajoebob Supports this as a Fact.

Horse hockey.

You cannot prove that something does not exist. “There is no God.” Show me proof.

The phrase refers to the concept of a scientific proof, not winning an argument. A scientific proof is considered absolute. Some proofs, like maths and geometry, are arguments that begins with known facts, proceeds from there through a series of logical deductions, and ends with the thing you’re trying to prove. In statistics, you present a hypothesis, you test it, you either say you can’t show it to be true or that you have demonstrated it to be (QED). THAT is a proof, not “dogs are the best pet because more people own dogs than any other animal.”

You cannot, logically, prove a negative. A reductio ad absurdum example is “Prove that Thomas DiMichele is not a murderer.” Unless you can produce incontrovertible evidence that Mr DiMichele has not ever, at any nanosecond of his life committed homicide, you cannot prove that statement. Another: “No one goes there anymore.” First, the lack of time frame is obvious. Second, the only way to prove that is to go there to observe, which then disproves that statement.

Best: “You can’t prove a negative” is not a fact. While i can expound on my examples of a scientific proof into a proof of it’s own, I cannot say there is NOT evidence showing it is NOT a fact. Lack of positive evidence is not the same as disproving it.

A preponderance of evidence is NOT proof. It is simply an argument to support the general validity of a statement.

Gary Hitch Supports this as a Fact.

1. The article states, ” it is hard to provide proof that a giant flying invisible pink unicorn name Terry-corn isn’t… because it isn’t and thus our best evidence is the absolute lack of evidence.”

This is a poor example. Invisible pink (or any other color) anything’s, are easy to disprove. Pink is a frequency in the electromagnetic spectrum. If something is invisible, by definition it is not reflecting any frequency at all. If it’s pink, it’s frequency is being reflected and it cannot be invisible. For this old argument to work, you have to posit some mechanism which is impeding the reflection of the color from the view of some observer. But that doesn’t make the unicorn invisible in itself. That only adds a factor that hides it from light-dependent vision systems like human eyes.

It may seem like quibbling but it’s important.

2. You also state: “If one claims, “all that is is, but God exists outside of that” then the argument for God becomes ontological, theological, metaphysic, and faith-based”

This is wrong. That claim that God exists outside of nature – all ELSE that exists is a logical conclusion, not a faith-based claim. By definition, God created ALL ELSE that exists and therefore he NECESSARILY exists “outside”, “beyond”, or transcendent to it.

“Faith-based metaphysical arguments don’t require scientific empirical evidence…”

a) Your entire article is based on metaphysics. Logic and reason are metaphysical!

b) You have an incorrect idea of what proper faith is. Faith is trust. Trust must be based on evidence of trustworthiness. And that is of course, the very kind of faith promoted in Christianity for example. Nowhere are people demanded to just blindly believe without reason, in the bible. On the contrary, we are told, “Come let us reason together” – by God. We are also told that Christ left his followers “many infallible proofs” of his resurrection. He had no such stupid idea of faith that they ought to just believe it without evidence.

Your view in this looks like the ubiquitous error made by atheists everywhere concerning the meaning of faith as some sort of blind, irrational leap in the dark. That is not real faith. That is stupidity. That blind kind of “faith’ can exist, of course. But that kind is foolishness.

“unless they try to posit something that can be debunked by empirical science (in that case, arguments for faith instead of reason tend to be logically “weak,” in that they lack supporting evidence)”

Anything that can be debunked by empirical or other forms of evidence cannot be logically weak. Lacking supporting evidence does equate to “weak”. An argument may be air-tight logically, yet without any empirical evidence. The proof of God, for example, is logical, not empirical. Unless one denies the validity of logic, that proof, if well reasoned, is as good as any other. Else we ought to abandon mathematics which is based on logic.

Moreover, you seem to be neglecting the fact that you cannot trust your own faculties of reason without having some good evidence that they are presenting the real world, correct logic etc. You cannot test your brain using your brain. So how do we know that our own faculties of reason are trustworthy? Under a purely materialist view, we do not and cannot. The reliability of reason itself must be taken as an article of faith. An axiom of reason, if you will. Else nothing is knowable at all. We may well all be nothing but clumps of cells in a Matrix, being fed illusory images and sensations of some algorithmic origin, in such a case. The idea that we are all mere bags of chemicals, sacks of meat, packs of neurons, destroys any possibility of objective reasoning being known to be reliable. It destroys objective rationality.

Thanks for the thoughtful response. I’ll think on it.

Fernando Did not vote.

If all we know for sure is that we know nothing for sure, how can we be sure we know nothing for sure?

I’m sure I do not know…. or am I?

Vernon McVety Jr. Doesn't beleive this myth.

THE OLD PSEUDOLOGICAL STATEMENT “I CAN’T PROVE A NEGATIVE” has been used mostly by those who have a hard time dealing with truths that they either can’t handle or don’t want to. In this current age and post truth climate of “fake news” and fact manipulation this fallacious statement is used by a lot of media people and reporters when truth thirsty people aren’t letting them do their jobs the way they want. I like this essay so much I posted it on Facebook.

Glad to hear the feedback. Thank you for sharing it!

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What 'Fail to Reject' Means in a Hypothesis Test

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In statistics , scientists can perform a number of different significance tests to determine if there is a relationship between two phenomena. One of the first they usually perform is a null hypothesis test. In short, the null hypothesis states that there is no meaningful relationship between two measured phenomena. After a performing a test, scientists can:

• Reject the null hypothesis (meaning there is a definite, consequential relationship between the two phenomena), or
• Fail to reject the null hypothesis (meaning the test has not identified a consequential relationship between the two phenomena)

Key Takeaways: The Null Hypothesis

• In a test of significance, the null hypothesis states that there is no meaningful relationship between two measured phenomena.

• By comparing the null hypothesis to an alternative hypothesis, scientists can either reject or fail to reject the null hypothesis.

• The null hypothesis cannot be positively proven. Rather, all that scientists can determine from a test of significance is that the evidence collected does or does not disprove the null hypothesis.

It is important to note that a failure to reject does not mean that the null hypothesis is true—only that the test did not prove it to be false. In some cases, depending on the experiment, a relationship may exist between two phenomena that is not identified by the experiment. In such cases, new experiments must be designed to rule out alternative hypotheses.

Null vs. Alternative Hypothesis

The null hypothesis is considered the default in a scientific experiment . In contrast, an alternative hypothesis is one that claims that there is a meaningful relationship between two phenomena. These two competing hypotheses can be compared by performing a statistical hypothesis test, which determines whether there is a statistically significant relationship between the data.

For example, scientists studying the water quality of a stream may wish to determine whether a certain chemical affects the acidity of the water. The null hypothesis—that the chemical has no effect on the water quality—can be tested by measuring the pH level of two water samples, one of which contains some of the chemical and one of which has been left untouched. If the sample with the added chemical is measurably more or less acidic—as determined through statistical analysis—it is a reason to reject the null hypothesis. If the sample's acidity is unchanged, it is a reason to not reject the null hypothesis.

When scientists design experiments, they attempt to find evidence for the alternative hypothesis. They do not try to prove that the null hypothesis is true. The null hypothesis is assumed to be an accurate statement until contrary evidence proves otherwise. As a result, a test of significance does not produce any evidence pertaining to the truth of the null hypothesis.

Failing to Reject vs. Accept

In an experiment, the null hypothesis and the alternative hypothesis should be carefully formulated such that one and only one of these statements is true. If the collected data supports the alternative hypothesis, then the null hypothesis can be rejected as false. However, if the data does not support the alternative hypothesis, this does not mean that the null hypothesis is true. All it means is that the null hypothesis has not been disproven—hence the term "failure to reject." A "failure to reject" a hypothesis should not be confused with acceptance.

In mathematics, negations are typically formed by simply placing the word “not” in the correct place. Using this convention, tests of significance allow scientists to either reject or not reject the null hypothesis. It sometimes takes a moment to realize that “not rejecting” is not the same as "accepting."

Null Hypothesis Example

In many ways, the philosophy behind a test of significance is similar to that of a trial. At the beginning of the proceedings, when the defendant enters a plea of “not guilty,” it is analogous to the statement of the null hypothesis. While the defendant may indeed be innocent, there is no plea of “innocent” to be formally made in court. The alternative hypothesis of “guilty” is what the prosecutor attempts to demonstrate.

The presumption at the outset of the trial is that the defendant is innocent. In theory, there is no need for the defendant to prove that he or she is innocent. The burden of proof is on the prosecuting attorney, who must marshal enough evidence to convince the jury that the defendant is guilty beyond a reasonable doubt. Likewise, in a test of significance, a scientist can only reject the null hypothesis by providing evidence for the alternative hypothesis.

If there is not enough evidence in a trial to demonstrate guilt, then the defendant is declared “not guilty.” This claim has nothing to do with innocence; it merely reflects the fact that the prosecution failed to provide enough evidence of guilt. In a similar way, a failure to reject the null hypothesis in a significance test does not mean that the null hypothesis is true. It only means that the scientist was unable to provide enough evidence for the alternative hypothesis.

For example, scientists testing the effects of a certain pesticide on crop yields might design an experiment in which some crops are left untreated and others are treated with varying amounts of pesticide. Any result in which the crop yields varied based on pesticide exposure—assuming all other variables are equal—would provide strong evidence for the alternative hypothesis (that the pesticide does affect crop yields). As a result, the scientists would have reason to reject the null hypothesis.

• Type I and Type II Errors in Statistics
• Null Hypothesis and Alternative Hypothesis
• An Example of Chi-Square Test for a Multinomial Experiment
• The Difference Between Type I and Type II Errors in Hypothesis Testing
• What Level of Alpha Determines Statistical Significance?
• What Is the Difference Between Alpha and P-Values?
• How to Find Critical Values with a Chi-Square Table
• The Runs Test for Random Sequences
• An Example of a Hypothesis Test
• What Is ANOVA?
• Example of a Permutation Test
• Degrees of Freedom for Independence of Variables in Two-Way Table
• How to Find Degrees of Freedom in Statistics
• Example of an ANOVA Calculation
• Confidence Intervals: 4 Common Mistakes
• How to Construct a Confidence Interval for a Population Proportion

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Definition of disprove

transitive verb

Examples of disprove in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'disprove.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle English, from Anglo-French desprover , from des- dis- + prover to prove

14th century, in the meaning defined above

Dictionary Entries Near disprove

disprovable

disprovided

Cite this Entry

“Disprove.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/disprove. Accessed 9 Sep. 2024.

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Meaning of disprove in English

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• actions speak louder than words idiom
• anti-sexist
• authenticate
• demonstrable
• demonstrably
• demonstration of something
• do justice to someone/something idiom
• non-evidence
• proof positive
• provability
• vindication
• vindicative
• vindicatory

disprove | American Dictionary

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• 1.1 Etymology
• 1.2 Pronunciation
• 1.3.1 Usage notes
• 1.3.2 Antonyms
• 1.3.3 Translations
• 1.4 Anagrams

From Middle English disproven , dispreven , from Old French desprover , from des- + prover , equivalent to dis- +‎ prove .

Pronunciation

enPR : dĭs-pro͞ovʹ

• Rhymes: -uːv

disprove ( third-person singular simple present disproves , present participle disproving , simple past disproved , past participle disproved or disproven )

• To prove to be false or erroneous ; to confute ; to refute . disprove a theory disprove a hypothesis

Usage notes

• The past participle disproven is often proscribed in favor of disproved .

Translations

• English terms inherited from Middle English
• English terms derived from Middle English
• English terms derived from Old French
• English terms prefixed with dis-
• English 2-syllable words
• English terms with IPA pronunciation
• English terms with audio links
• Rhymes:English/uːv
• Rhymes:English/uːv/2 syllables
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