Hi! So, yours is actually a sophisticated question that masquerades as a simple one, so I'll try to answer this in a way that conveys the concept while perhaps alluding to some of its problems. At its heart, the null hypothesis is a sort of "straw man" that is defined by a researcher at the beginning of an experiment that usually represents a state of affairs that would be expected to occur if the researcher's proposal were false. Note that a null hypothesis is entirely imaginary, and it has nothing to do with the actual state of the world. It is contrived, usually to show that the actual state of the world is inconsistent with the null hypothesis. Suppose a researcher is trying to determine whether the heights of men and women are different. A suitable null hypothesis might be that the difference of the two population averages (height of men and height of women) is equal to zero. Then the researcher would conduct his or her experiment by measuring the heights of many men and women. When it comes time to draw a statistical conclusion, he or she will compute the probability that the observed data (the set of heights) could have come from the null hypothesis (i.e., a world where there is no difference). This probability is called a "p-value". Conceptually, this is similar to a "proof by contradiction," in which we assert that, if the probability is very small that the data could have originated from the null hypothesis, it must not be true. This is what is meant by "rejecting the null hypothesis". It is different from a proof by contradiction because rejecting the null proves nothing, except perhaps that the null is unlikely to be the source of the observed data. It doesn't prove that the true difference is 5 inches, or 1 inch, or anything. Because of this, rejecting the null hypothesis is in NO WAY equivalent to accepting an alternative hypothesis. Usually, in the course of an experiment, we observe a result (such as the observed height difference, perhaps it is ~5 inches) that, once we reject, replaces the hypothesized value of 0 under the null. However, we DON'T know anything about the probability that our observed value is "correct", which is why we never say that we have "accepted" an alternative. I actually hesitate to discuss an "alternative" hypothesis because most researchers never state one and it doesn't matter for the purposes of null hypothesis significance testing (NHST). It is just the name given to the conclusion drawn by the researchers after they have rejected their null hypothesis. Philosophically, there is an adage that data can never be used to prove an assertion, only to disprove one. It includes an analogy about a turkey concluding that he is loved by his human family and is proven wrong upon being slaughtered on Thanksgiving. I'll include a link if I can find it. Now, think about this: The concept of rejecting a null hypothesis probably seems very reasonable as long as we are careful not to overinterpret it, and this is how NHST was performed for decades. But consider - what is the probability that the null hypothesis is true in the first place? In other words, how likely is it that the difference between mens' and womens' heights is equal to zero? I propose that the probability is exactly zero, and if you disagree then I will find a ruler small enough to prove me correct. The difference can never be equal to exactly zero (even though this is the "straw man" that our experiment refutes), so we are effectively testing against a hypothesis that can never be true. Rejecting a hypothesis we already know to be false tells us nothing important ("the data are unlikely to have come from this state that cannot be true"). And since every null hypothesis is imaginary, it is suggested that any null hypothesis can be rejected with enough statistical power (read:sample size). Often a "significant" result says more about a study's sample size than it does about the study's findings, even though the language used in papers/media suggests to readers that the findings are more "important" or "likely to be correct". This has, in part, led to a reproducibility crisis in the sciences and, for some, an undermining of subject-matter-experts' trust in the use of applied statistics. Answer from stat_daddy on reddit.com
National University
resources.nu.edu › statsresources › hypothesis
Null & Alternative Hypotheses - Statistics Resources - LibGuides at National University
October 27, 2025 - Null Hypothesis: H0: There is no difference in the salary of factory workers based on gender.
Scribbr
scribbr.com › home › null and alternative hypotheses | definitions & examples
Null & Alternative Hypotheses | Definitions, Templates & Examples
January 24, 2025 - Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept. Be careful not to say you “prove” or “accept” the null hypothesis. Example: Population on trialThink of a statistical test as being like a legal trial.
Null hypothesis and Alternative Hypothesis
Hi! So, yours is actually a sophisticated question that masquerades as a simple one, so I'll try to answer this in a way that conveys the concept while perhaps alluding to some of its problems. At its heart, the null hypothesis is a sort of "straw man" that is defined by a researcher at the beginning of an experiment that usually represents a state of affairs that would be expected to occur if the researcher's proposal were false. Note that a null hypothesis is entirely imaginary, and it has nothing to do with the actual state of the world. It is contrived, usually to show that the actual state of the world is inconsistent with the null hypothesis. Suppose a researcher is trying to determine whether the heights of men and women are different. A suitable null hypothesis might be that the difference of the two population averages (height of men and height of women) is equal to zero. Then the researcher would conduct his or her experiment by measuring the heights of many men and women. When it comes time to draw a statistical conclusion, he or she will compute the probability that the observed data (the set of heights) could have come from the null hypothesis (i.e., a world where there is no difference). This probability is called a "p-value". Conceptually, this is similar to a "proof by contradiction," in which we assert that, if the probability is very small that the data could have originated from the null hypothesis, it must not be true. This is what is meant by "rejecting the null hypothesis". It is different from a proof by contradiction because rejecting the null proves nothing, except perhaps that the null is unlikely to be the source of the observed data. It doesn't prove that the true difference is 5 inches, or 1 inch, or anything. Because of this, rejecting the null hypothesis is in NO WAY equivalent to accepting an alternative hypothesis. Usually, in the course of an experiment, we observe a result (such as the observed height difference, perhaps it is ~5 inches) that, once we reject, replaces the hypothesized value of 0 under the null. However, we DON'T know anything about the probability that our observed value is "correct", which is why we never say that we have "accepted" an alternative. I actually hesitate to discuss an "alternative" hypothesis because most researchers never state one and it doesn't matter for the purposes of null hypothesis significance testing (NHST). It is just the name given to the conclusion drawn by the researchers after they have rejected their null hypothesis. Philosophically, there is an adage that data can never be used to prove an assertion, only to disprove one. It includes an analogy about a turkey concluding that he is loved by his human family and is proven wrong upon being slaughtered on Thanksgiving. I'll include a link if I can find it. Now, think about this: The concept of rejecting a null hypothesis probably seems very reasonable as long as we are careful not to overinterpret it, and this is how NHST was performed for decades. But consider - what is the probability that the null hypothesis is true in the first place? In other words, how likely is it that the difference between mens' and womens' heights is equal to zero? I propose that the probability is exactly zero, and if you disagree then I will find a ruler small enough to prove me correct. The difference can never be equal to exactly zero (even though this is the "straw man" that our experiment refutes), so we are effectively testing against a hypothesis that can never be true. Rejecting a hypothesis we already know to be false tells us nothing important ("the data are unlikely to have come from this state that cannot be true"). And since every null hypothesis is imaginary, it is suggested that any null hypothesis can be rejected with enough statistical power (read:sample size). Often a "significant" result says more about a study's sample size than it does about the study's findings, even though the language used in papers/media suggests to readers that the findings are more "important" or "likely to be correct". This has, in part, led to a reproducibility crisis in the sciences and, for some, an undermining of subject-matter-experts' trust in the use of applied statistics. More on reddit.com
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January 5, 2021What is a null hypothesis?
The null hypothesis is often used in hypothesis testing, where researchers aim to determine if there is enough evidence to support an alternative hypothesis (denoted as Ha) that suggests there is a significant difference or relationship. The null hypothesis, in contrast, assumes that any observed difference or relationship is due to random chance or sampling error. A concrete example ... More on findtutors.co.uk
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ELI5 what is the null hypothesis and can you give me some simple examples?
More or less, the null hypothesis is a hypothesis that states there wasn't anything important discovered in observation. If it's a two-group trial and control study, the null hypothesis is generally "the trial group is no different".
If the study is testing a medication, the null hypothesis is "it doesn't do anything".
If the study is comparing gender differences in some mental task, the null hypothesis is "there isn't a difference".
More on reddit.com 13
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November 23, 2021Eli5: What is a null hypothesis and how do type 1 and type 2 errors work.
The null hypothesis, in statistics, is the idea that there is no significant difference between two populations. If, for example, you're testing whether your company's fancy new insect killing chemical is more effective than the competition, you might run some tests in two groups: one using a reference chemical (or nothing at all), and one using your new one. The null hypothesis is "This chemical is no more effective than what was used in the other group." The alternative hypothesis is that it is more effective. "Type I" and "Type II" errors can be thought of a lot easier by their colloquial names: "false positive" and "false negative." The false positive is where you erroneously believe your chemical is more effective (when it actually isn't), and the false negative is where you erroneously believe your chemical is not more effective (when it actually is). In statistics, these errors occur due to improperly selected cutoff values (how are we judging effectiveness) and confidences (how many tests are we doing). More on reddit.com
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July 3, 2021What are some problems with the null hypothesis?
One major problem with the null hypothesis is that researchers typically will assume that accepting the null is a failure of the experiment. However, accepting or rejecting any hypothesis is a positive result. Even if the null is not refuted, the researchers will still learn something new.
simplypsychology.org
simplypsychology.org › research methodology › what is the null hypothesis & when do you reject the null hypothesis
What Is The Null Hypothesis & When To Reject It
What is the difference between a null hypothesis and an alternative hypothesis?
The alternative hypothesis is the complement to the null hypothesis. The null hypothesis states that there is no effect or no relationship between variables, while the alternative hypothesis claims that there is an effect or relationship in the population.
It is the claim that you expect or hope will be true. The null hypothesis and the alternative hypothesis are always mutually exclusive, meaning that only one can be true at a time.
It is the claim that you expect or hope will be true. The null hypothesis and the alternative hypothesis are always mutually exclusive, meaning that only one can be true at a time.
simplypsychology.org
simplypsychology.org › research methodology › what is the null hypothesis & when do you reject the null hypothesis
What Is The Null Hypothesis & When To Reject It
Why can a null hypothesis not be accepted?
We can either reject or fail to reject a null hypothesis, but never accept it. If your test fails to detect an effect, this is not proof that the effect doesn’t exist. It just means that your sample did not have enough evidence to conclude that it exists.
We can’t accept a null hypothesis because a lack of evidence does not prove something that does not exist. Instead, we fail to reject it.
Failing to reject the null indicates that the sample did not provide sufficient enough evidence to conclude that an effect exists.
If the p-value is greater than the significance level, then you fail to reject the null hypothesis.
We can’t accept a null hypothesis because a lack of evidence does not prove something that does not exist. Instead, we fail to reject it.
Failing to reject the null indicates that the sample did not provide sufficient enough evidence to conclude that an effect exists.
If the p-value is greater than the significance level, then you fail to reject the null hypothesis.
simplypsychology.org
simplypsychology.org › research methodology › what is the null hypothesis & when do you reject the null hypothesis
What Is The Null Hypothesis & When To Reject It
Videos
03:45
Null Hypothesis | Definition & Examples - Video | Study.com
Examples of null and alternative hypotheses (video)
07:12
Hypothesis vs Null Hypothesis and Statistics - YouTube
03:49
Null and alternative hypotheses with Lindsey Leach - YouTube
54:09
Null Hypothesis Significance Testing - YouTube
ThoughtCo
thoughtco.com › null-hypothesis-examples-609097
How to Formulate a Null Hypothesis (With Examples)
May 7, 2024 - In addition to the null hypothesis, the alternative hypothesis is also a staple in traditional significance tests. It's essentially the opposite of the null hypothesis because it assumes the claim in question is true. For the first item in the table above, for example, an alternative hypothesis might be "Age does have an effect on mathematical ability."
Investopedia
investopedia.com › terms › n › null_hypothesis.asp
Null Hypothesis: What Is It and How Is It Used in Investing?
May 8, 2025 - We can then compare the (calculated) sample mean to the (hypothesized) population mean of 7.0 and attempt to reject the null hypothesis. (The null hypothesis here—that the population mean is not 7.0—cannot be proved using the sample data. It can only be rejected.) Take another example: The annual return of a particular mutual fund is claimed to be 8%. Assume that the mutual fund has been in existence for 20 years.
Reddit
reddit.com › r/askstatistics › null hypothesis and alternative hypothesis
r/AskStatistics on Reddit: Null hypothesis and Alternative Hypothesis
January 5, 2021 -
Hey! Can someone explain to me in simple terms the definition of null hypothesis? If u can use an example it would be great! Also if we reject the null hypothesis does it mean that the alternative hypothesis is true?
Top answer 1 of 4
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Hi! So, yours is actually a sophisticated question that masquerades as a simple one, so I'll try to answer this in a way that conveys the concept while perhaps alluding to some of its problems. At its heart, the null hypothesis is a sort of "straw man" that is defined by a researcher at the beginning of an experiment that usually represents a state of affairs that would be expected to occur if the researcher's proposal were false. Note that a null hypothesis is entirely imaginary, and it has nothing to do with the actual state of the world. It is contrived, usually to show that the actual state of the world is inconsistent with the null hypothesis. Suppose a researcher is trying to determine whether the heights of men and women are different. A suitable null hypothesis might be that the difference of the two population averages (height of men and height of women) is equal to zero. Then the researcher would conduct his or her experiment by measuring the heights of many men and women. When it comes time to draw a statistical conclusion, he or she will compute the probability that the observed data (the set of heights) could have come from the null hypothesis (i.e., a world where there is no difference). This probability is called a "p-value". Conceptually, this is similar to a "proof by contradiction," in which we assert that, if the probability is very small that the data could have originated from the null hypothesis, it must not be true. This is what is meant by "rejecting the null hypothesis". It is different from a proof by contradiction because rejecting the null proves nothing, except perhaps that the null is unlikely to be the source of the observed data. It doesn't prove that the true difference is 5 inches, or 1 inch, or anything. Because of this, rejecting the null hypothesis is in NO WAY equivalent to accepting an alternative hypothesis. Usually, in the course of an experiment, we observe a result (such as the observed height difference, perhaps it is ~5 inches) that, once we reject, replaces the hypothesized value of 0 under the null. However, we DON'T know anything about the probability that our observed value is "correct", which is why we never say that we have "accepted" an alternative. I actually hesitate to discuss an "alternative" hypothesis because most researchers never state one and it doesn't matter for the purposes of null hypothesis significance testing (NHST). It is just the name given to the conclusion drawn by the researchers after they have rejected their null hypothesis. Philosophically, there is an adage that data can never be used to prove an assertion, only to disprove one. It includes an analogy about a turkey concluding that he is loved by his human family and is proven wrong upon being slaughtered on Thanksgiving. I'll include a link if I can find it. Now, think about this: The concept of rejecting a null hypothesis probably seems very reasonable as long as we are careful not to overinterpret it, and this is how NHST was performed for decades. But consider - what is the probability that the null hypothesis is true in the first place? In other words, how likely is it that the difference between mens' and womens' heights is equal to zero? I propose that the probability is exactly zero, and if you disagree then I will find a ruler small enough to prove me correct. The difference can never be equal to exactly zero (even though this is the "straw man" that our experiment refutes), so we are effectively testing against a hypothesis that can never be true. Rejecting a hypothesis we already know to be false tells us nothing important ("the data are unlikely to have come from this state that cannot be true"). And since every null hypothesis is imaginary, it is suggested that any null hypothesis can be rejected with enough statistical power (read:sample size). Often a "significant" result says more about a study's sample size than it does about the study's findings, even though the language used in papers/media suggests to readers that the findings are more "important" or "likely to be correct". This has, in part, led to a reproducibility crisis in the sciences and, for some, an undermining of subject-matter-experts' trust in the use of applied statistics.
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The null hypothesis (Ho) signifies no change. The alternative hypothesis (Ha) signifies a change. If we reject the null, we have evidence for the alternative hypothesis. This doesn’t mean that it’s true just that within this study, we have evidence to support the alternative hypothesis. If we fail to reject the null (we don’t use the word accept) then there is not enough evidence supporting the alternative hypothesis. Example: I’m wondering if smoking impacts lung function using a spirometry test that measures forced exploratory volume per second (FEV1). Ho: There is no difference in FEV1 between smokers vs non smokers Ha: There is a difference in FEV1 between smokers and non smokers. Rejecting or failing to reject the null aka Ho will involve more steps than just analyzing the mean FEV1 between the two groups, so let’s stop here before we get into more hypothesis testing.
Simply Psychology
simplypsychology.org › research methodology › what is the null hypothesis & when do you reject the null hypothesis
What Is The Null Hypothesis & When To Reject It
July 31, 2023 - We reject the null hypothesis when the data provide strong enough evidence to conclude that it is likely incorrect. This often occurs when the p-value (probability of observing the data given the null hypothesis is true) is below a predetermined significance level.
Statology
statology.org › home › how to write a null hypothesis (5 examples)
How to Write a Null Hypothesis (5 Examples)
March 10, 2021 - Here is how to write the null and alternative hypotheses for this scenario: H0: p ≥ .30 (the true proportion of citizens who support the law is greater than or equal to 30%) HA: μ < 0.30 (the true proportion of citizens who support the law is less than 30%) Introduction to Hypothesis Testing Introduction to Confidence Intervals An Explanation of P-Values and Statistical Significance
Nc3rs
eda.nc3rs.org.uk › experimental-design-experiment
Understanding your experiment | NC3Rs EDA
The null hypothesis, or H0, represents the hypothesis of no change or no effect. In other words, the response being measured is unaffected by the experimental manipulation being tested. For example, if the effect of a proposed anti-cholesterol drug on blood pressure is being tested, then the null hypothesis could be that the drug treatment has no effect on the measured blood pressure:
FindTutors
findtutors.co.uk › questions › maths › what---null-hypothesis-20238
What is a null hypothesis?
August 15, 2023 - ... The null hypothesis in statistics is the assumed probability. For example if you're testing the fairness of a dice, the null hypothesis would be H0: p = ⅙ as that is what is should be if it's fair.
Lumen Learning
courses.lumenlearning.com › introstats1 › chapter › null-and-alternative-hypotheses
Null and Alternative Hypotheses | Introduction to Statistics
Test if the percentage of U.S. students who take advanced placement exams is more than 6.6%. State the null and alternative hypotheses. ... On a state driver’s test, about 40% pass the test on the first try. We want to test if more than 40% pass on the first try. Fill in the correct symbol (=, ≠, ≥, <, ≤, >) for the null and alternative hypotheses. H0: p __ 0.40 Ha: p __ 0.40 ... In a hypothesis test, sample data is evaluated in order to arrive at a decision about some type of claim.
Optimizely
optimizely.com › optimization-glossary › null-hypothesis
What is a null hypothesis? - Optimizely
June 10, 2025 - In marketing, you might test whether "changing the color of the 'Subscribe' button from red to green does not affect conversion rates." Here the null hypothesis assumes both colors have identical conversion rates. While educators could test "there is no difference in average test scores between students using the new teaching method versus the traditional method." These examples highlight why the null hypothesis framework is essential for data-driven decision-making.
Laerd Statistics
statistics.laerd.com › statistical-guides › hypothesis-testing-3.php
Hypothesis Testing - Significance levels and rejecting or accepting the null hypothesis
This can happen, for example, when biological activity/presence in measured. That is, a protein might be "dormant" and the stimulus you are using can only possibly "wake it up" (i.e., it cannot possibly reduce the activity of a "dormant" protein). In addition, for some statistical tests, one-tailed tests are not possible. Let's return finally to the question of whether we reject or fail to reject the null hypothesis.
YouTube
youtube.com › watch
Writing the Null and Alternate Hypothesis in Statistics - YouTube
Welcome to our comprehensive YouTube video on writing the null and alternate hypotheses in statistics! In this enlightening tutorial, we delve into the world...
Published August 28, 2023
BYJUS
byjus.com › maths › null-hypothesis
Null Hypothesis Definition
April 25, 2022 - This type of hypothesis does not define the exact value of the parameter. But it denotes a specific range or interval. For example 45< μ <60 · Sometimes the null hypothesis is rejected too. If this hypothesis is rejected means, that research could be invalid.
YouTube
youtube.com › watch
What's a null hypothesis? // How to write a null hypothesis - YouTube
One way to say this is: A null hypothesis is a statement that says how the independent variable would have no effect on the dependent variable. Watch the vid...
Published February 11, 2025
PubMed Central
pmc.ncbi.nlm.nih.gov › articles › PMC6785820
An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors - PMC
For superiority studies, the alternate ... example, in the ABLE study, we start by stating the null hypothesis—there is no difference in mortality between groups receiving fresh RBCs and standard-issue RBCs....
Scribd
scribd.com › document › 494845048 › Null-Hypothesis-Examples-docx
Null Hypothesis Examples | PDF | Null Hypothesis | Hypothesis
Examples of null hypotheses include "Age has no effect on mathematical ability" and "Taking aspirin daily does not affect heart attack risk." The null hypothesis is denoted as H0 and is what researchers attempt to find evidence against through ...
San Jose State University
sjsu.edu › faculty › gerstman › StatPrimer › hyp-test.pdf pdf
6: Introduction to Null Hypothesis Significance Testing
The null hypothesis (H0) is a statement of “no difference,” “no association,” or “no treatment effect.” ... The alternative hypothesis, Ha is a statement of “difference,” “association,” or “treatment effect.” · H0 is assumed to be true until proven otherwise. However, Ha is the hypothesis the researcher hopes to ... Take as an example a treatment that is said to be 25% effective.
GeeksforGeeks
geeksforgeeks.org › data science › z-test
Z-test : Formula, Types, Examples - GeeksforGeeks
A consumer group tests 100 phones and finds the average battery life to be 11.8 hours with a population standard deviation of 0.5 hours. At a 5% significance level, is there evidence to refute the company's claim?
Published July 24, 2025
Wikipedia
en.wikipedia.org › wiki › Null_hypothesis
Null hypothesis - Wikipedia
3 weeks ago - Fisher's original (lady tasting tea) example was a one-tailed test. The null hypothesis was asymmetric. The probability of guessing all cups correctly was the same as guessing all cups incorrectly, but Fisher noted that only guessing correctly was compatible with the lady's claim.