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
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
30
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.
National University
resources.nu.edu › statsresources › hypothesis
Null & Alternative Hypotheses - Statistics Resources - LibGuides at National University
October 27, 2025 - Alternative Hypothesis (Ha) – This is also known as the claim. This hypothesis should state what you expect the data to show, based on your research on the topic. This is your answer to your research question. ... Null Hypothesis: H0: There is no difference in the salary of factory workers based on gender.
What is the difference between "testing of hypothesis" and "test of significance"? - Cross Validated
Significance testing is what Fisher devised and hypothesis testing is what Neyman and Pearson devised to replace significance testing. They are not the same and are mutually incompatible to an extent that would surprise most users of null hypothesis tests. More on stats.stackexchange.com
Eli5: 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, 2021Null 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, 2021ELI5: What is the Null hypothesis?
The null hypothesis says "there is nothing going on here". Let's say you rolled a die three times and it always came up six. You conclude the die is probably loaded, and that becomes your hypothesis. The null hypothesis is the die is not loaded, and your results were due to chance or experimental error. After all, there is a 1 in 216 chance you would expect to see that on a fair die. You would next design an experiment to drive the likelihood of the null hypothesis so low that it can be ignored. If you rolled the dice ten times and got all sixes, that would represent a 1 in 60 million longshot, and strong evidence the null hypothesis is not true. Note that evidence against the null hypothesis is evidence something is going on, but not necessarily evidence for your hypothesis. Your hypothesis could have just as easily been "I am controlling the die with my telekineses", and eliminating the null hypothesis certainly would not support that conclusion. Further experiments would be required to determine exactly when the die always rolls sixes. The null hypothesis is often tied to the placebo effect. When people receive treatment, they tend to report improvement even when there is none. The null hypothesis remains "there is nothing going on here", and that any results were due to chance and/or the placebo effect. More on reddit.com
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June 28, 2017What’s the difference between a research hypothesis and a statistical hypothesis?
A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“x affects y because …”). · A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study, the statistical hypotheses correspond logically to the research hypothesis.
scribbr.com
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Null & Alternative Hypotheses | Definitions, Templates & Examples
What is hypothesis testing?
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses, by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
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Null & Alternative Hypotheses | Definitions, Templates & Examples
What are null and alternative hypotheses?
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.
scribbr.com
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Null & Alternative Hypotheses | Definitions, Templates & Examples
Videos
Examples of null and alternative hypotheses (video)
04:35
Null Hypothesis Vs Alternative Hypothesis (Easy Explanation) - YouTube
03:49
Null and alternative hypotheses with Lindsey Leach - YouTube
14:41
Hypothesis Testing and The Null Hypothesis - YouTube
06:52
Hypothesis Testing - Null and Alternative Hypotheses - YouTube
04:29
What's a null hypothesis? // How to write a null hypothesis - YouTube
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
Scribbr
scribbr.com › home › null and alternative hypotheses | definitions & examples
Null & Alternative Hypotheses | Definitions, Templates & Examples
January 24, 2025 - A null hypothesis claims that there is no effect in the population, while an alternative hypothesis claims that there is an effect.
Wikipedia
en.wikipedia.org › wiki › Null_hypothesis
Null hypothesis - Wikipedia
3 weeks ago - {\textstyle H_{0}} ) is the claim in scientific research that the effect being studied does not exist. The null hypothesis can also be described as the hypothesis in which no relationship exists between two sets of data or variables being analyzed. If the null hypothesis is true, any experimentally ...
Lumen Learning
courses.lumenlearning.com › introstats1 › chapter › null-and-alternative-hypotheses
Null and Alternative Hypotheses | Introduction to Statistics
If certain conditions about the sample are satisfied, then the claim can be evaluated for a population. In a hypothesis test, we: Evaluate the null hypothesis, typically denoted with H0. The null is not rejected unless the hypothesis test shows otherwise.
Wikipedia
en.wikipedia.org › wiki › Statistical_hypothesis_test
Statistical hypothesis test - Wikipedia
3 weeks ago - When the null hypothesis is predicted by theory, a more precise experiment will be a more severe test of the underlying theory. When the null hypothesis defaults to "no difference" or "no effect", a more precise experiment is a less severe test of the theory that motivated performing the experiment.
GeeksforGeeks
geeksforgeeks.org › data science › difference-between-null-and-alternate-hypothesis
Difference between Null and Alternate Hypothesis - GeeksforGeeks
May 18, 2022 - Null hypothesis suggests that there is no relationship between the two variables. Null hypothesis is also exactly the opposite of the alternative hypothesis. Null hypothesis is generally what researchers or scientists try to disprove and if the null hypothesis gets accepted then we have to make changes in our opinion i.e.
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Significance testing is what Fisher devised and hypothesis testing is what Neyman and Pearson devised to replace significance testing. They are not the same and are mutually incompatible to an extent that would surprise most users of null hypothesis tests.
Fisher's significance tests yield a p value that represents how extreme the observations are under the null hypothesis. That p value is an index of evidence against the null hypothesis and is the level of significance.
Neyman and Pearson's hypothesis tests set up both a null hypothesis and an alternative hypothesis and work as a decision rule for accepting the null hypothesis. Briefly (there is more to it than I can put here) you choose an acceptable rate of false positive inference, alpha (usually 0.05), and either accept or reject the null based on whether the p value is above or below alpha. You have to abide by the statistical test's decision if you wish to protect against false positive errors.
Fisher's approach allows you to take anything you like into account in interpreting the result, for example pre-existing evidence can be informally taken into account in the interpretation and presentation of the result. In the N-P approach that can only be done in the experimental design stage, and seems to be rarely done. In my opinion the Fisherian approach is more useful in basic bioscientific work than is the N-P approach.
There is a substantial literature about inconsistencies between significance testing and hypothesis testing and about the unfortunate hybridisation of the two. You could start with this paper: Goodman, Toward evidence-based medical statistics. 1: The P value fallacy. https://pubmed.ncbi.nlm.nih.gov/10383371/
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In many cases, these two statements mean the same thing. However, they can also be quite different.
Testing a hypothesis consists of first saying what you believe will occur with some phenomenon, then developing some kind of test for this phenomenon, and then determining whether or not the phenomenon actually occurred. In many cases, testing of a hypothesis need not involve any kind of statistical test. I am reminded of this quote by the physicist Ernest Rutherford - If your experiment needs statistics, you ought to have done a better experiment. That being said, testing of hypotheses normally does use some kind of statistical tool.
In contrast, testing of significance is a purely statistical concept. In essence, one has two hypotheses - the null hypothesis, which states that there is no difference between your two (or more) collections of data. The alternative hypothesis is that there is a difference between your two samples that did not occur by chance.
Based on the design of your study, you then compare the two (or more) samples using a statistical test, which gives you a number, which you then compare to a reference distribution (like the normal, t, or F distributions) and if this test statistic exceeds a critical value, you reject the null hypothesis and conclude that there is a difference between the two (or more) samples. This criterion is normally that the probability of the difference occurring by chance is less than one in twenty (p<0.05), though others are sometimes used.
Statistics LibreTexts
stats.libretexts.org › workbench › psyc 2200: elementary statistics for behavioral and social science (oja) without units › 7: inferential statistics and hypothesis testing
7.3: The Research Hypothesis and the Null Hypothesis - Statistics LibreTexts
June 12, 2024 - For most of this textbook, the null hypothesis is that the means of the two groups are similar. Much later, the null hypothesis will be that there is no relationship between the two groups.
PubMed Central
pmc.ncbi.nlm.nih.gov › articles › PMC6785820
An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors - PMC
In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the “alternate” hypothesis, and the opposite is called the “null” hypothesis; every study has a null hypothesis and an alternate ...
Laerd Statistics
statistics.laerd.com › statistical-guides › hypothesis-testing-3.php
Hypothesis Testing - Significance levels and rejecting or accepting the null hypothesis
In order to undertake hypothesis testing you need to express your research hypothesis as a null and alternative hypothesis. The null hypothesis and alternative hypothesis are statements regarding the differences or effects that occur in the population. You will use your sample to test which statement (i.e., the null hypothesis or alternative hypothesis) is most likely (although technically, you test the evidence against the null hypothesis).
ScienceDirect
sciencedirect.com › topics › mathematics › null-hypothesis-h0
Null Hypothesis (H0) - an overview | ScienceDirect Topics
The null hypothesis is often the more specific hypothesis of the two. For example, the null hypothesis might claim that the population mean is exactly equal to some known reference value or that an observed difference is just due to random chance.
Formpl
formpl.us › blog › alternative-null-hypothesis
Alternative vs Null Hypothesis: Pros, Cons, Uses & Examples
November 22, 2021 - The null hypothesis is capable of being tested, being verifiable, and also capable of being rejected. For example, if you want to conduct a study that will compare the relationship between project A and project B if the study is based on the assumption that both projects are of equal standard, the assumption is referred to as the null hypothesis.
ThoughtCo
thoughtco.com › null-hypothesis-vs-alternative-hypothesis-3126413
Differences Between The Null and Alternative Hypothesis
June 24, 2019 - This hypothesis is denoted by H0. The null hypothesis is what we attempt to find evidence against in our hypothesis test. We hope to obtain a small enough p-value that it is lower than our level of significance alpha and we are justified in rejecting the null hypothesis.
Applied Mathematics
colorado.edu › amath › sites › default › files › attached-files › lesson9_hyptests.pdf pdf
9 Hypothesis Tests (Ch 9.1-9.3, 9.5-9.9)
reject the null hypothesis;; for the other 81%, a type II error
iSixSigma
isixsigma.com › home › lean six sigma news › null hypothesis vs. hypothesis: what’s the difference?
Null Hypothesis vs. Hypothesis: What's the Difference? - isixsigma.com
The second is called the dependent variable, which is the measurable result. However, the biggest difference between the two is that a null hypothesis cannot be proven; it can only be rejected.
Published February 4, 2025
Outlier
articles.outlier.org › null-vs-alternative-hypothesis
Null vs. Alternative Hypothesis [Overview] | Outlier
April 28, 2023 - Alternative Hypothesis: The population mean is not equal to x. 𝝁 ≠ x · Null Hypothesis: The population mean is less than or equal to some number, x. 𝝁 ≤ x Alternative Hypothesis: The population mean is greater than x.
Statsig
statsig.com › perspectives › null-hypothesis-ab-testing
Understanding null hypothesis in A/B testing and experimentation
February 25, 2025 - In null hypothesis testing, the null hypothesis is the default assumption you need to disprove to establish statistical significance. If the data from your A/B test provides enough evidence to reject the null hypothesis, it means the observed differences between the control and variant groups are unlikely due to chance alone.
PubMed
pubmed.ncbi.nlm.nih.gov › 8900794
Hypothesis testing - PubMed
Hypothesis testing is the process of making a choice between two conflicting hypotheses. The null hypothesis, H0, is a statistical proposition stating that there is no significant difference between a hypothesized value of a population parameter and its value estimated from a sample drawn from that population.
365 Data Science
365datascience.com › blog › tutorials › statistics tutorials › hypothesis testing: null hypothesis and alternative hypothesis
Null Hypothesis and Alternative Hypothesis – 365 Data Science
September 19, 2025 - There are two hypotheses that are made: the null hypothesis, denoted H0, and the alternative hypothesis, denoted H1or HA.