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
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Null & Alternative Hypotheses | Definitions, Templates & Examples
January 24, 2025 - The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started. *Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p1 = p2. The alternative hypothesis (Ha) is the other answer to your research question.
National University
resources.nu.edu › statsresources › hypothesis
Null & Alternative Hypotheses - Statistics Resources - LibGuides at National University
October 27, 2025 - Null Hypothesis: H0: Experience on the job has no impact on the quality of a brick mason’s work. Alternative Hypothesis: Ha: The quality of a brick mason’s work is influenced by on-the-job experience.
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, 2021[College Statistics] Help with null/alternative hypothesis?
Hey! Really good questions. Rather than just answer you, I think it's important to explain definitions and why we need to ask for null/alternative hypotheses in statistics. First, in stats, we are trying to find mathematical grounds for believing there is an actual relationship or association between some phenomena. In your example: grocery prices at Walmart and grocery prices at Target. Your hypothesis, Walmart has cheaper groceries than Target, is the alternative hypothesis. Now, I know what you're thinking! What, how can this be? That is my hypothesis, not an alternative! Which is true, but hold on.
Second, when searching for evidence of this (or any) relationship, you are attempting to disprove or reject the null hypothesis, which sounds weirder than it is. "Null" means no significance, no relationship. This is the default position, where there is no relationship between the prices at Walmart and the prices at Target. Thus, the alternative is that there is some relationship. If your data reveal an association, you reject the null hypothesis; if analyses reveal no relationship between prices, you would not reject the null hypothesis. In stats, neither theory is "proven," but data must be analyzed and interpreted to determine how statistically significant, or likely/unlikely, the data are.
As for what test to run, I think you should use a "difference in means" hypothesis test, or a t-test. To do this, you'll need to pick your significance level (.01, .05, .10 are common, if you've been using one in class, go with that), and compute the standard deviation of each of the two samples (prices at (1) Walmart and (2) Target), the standard error, degrees of freedom, and t-value. With the t-value, you can calculate the P-value. This reflects the probability of finding a relationship as extreme as your data assuming the null is true. For example, if your P-value is p=.03, it indicates that if Walmart's prices are the same as Target's, one could expect to obtain your observed difference or more in 3% of studies due to random sampling error.
This website is a great resource. I hope this helps! Feel free to PM me if you have any questions. :)
More on reddit.com 3
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June 6, 2016A response to The Alternative Hypothesis video 'What the Experts Really Think About Race Realism and White Nationalism…'
Ryan Faulk has been hoarding racist 'science' for the last 10 years, he really needs to get a goddamn life. More on reddit.com
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December 30, 2017ELI5: Null and alternative hypothesis (statistics
I understand the basic premise of hypothesis testing. I under stand why we fail to reject or reject the null hypothesis. I can't wrap my head around… More on reddit.com
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August 14, 2015What’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)
03:49
Null and alternative hypotheses with Lindsey Leach - YouTube
09:50
Alternative Hypotheses: Main Ideas!!! - YouTube
06:52
Hypothesis Testing - Null and Alternative Hypotheses - YouTube
04:29
Hypothesis testing. Null vs alternative - YouTube
03:13
Null and Alternative Hypotheses in Statistics (7-8) - YouTube
GeeksforGeeks
geeksforgeeks.org › mathematics › alternative-hypothesis-definition-types-and-examples
Alternative Hypothesis: Definition, Types and Examples - GeeksforGeeks
August 30, 2025 - The null hypothesis, denoted Ho, is the default position where variables do not have a relation with each other. That means the null hypothesis is assumed true until evidence indicates otherwise. The alternative hypothesis, denoted H1, on the other hand, opposes the null hypothesis.
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?
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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.
Tallahassee State College
tsc.fl.edu › media › divisions › learning-commons › resources-by-subject › math › statistics › The-Null-and-the-Alternative-Hypotheses.pdf pdf
The Null and the Alternative Hypotheses
more than or less than 50%. The Null and Alternative Hypotheses looks like: H0: p = 0.5 (This is
Pressbooks
ecampusontario.pressbooks.pub › introstats › chapter › 8-2-null-and-alternative-hypotheses
8.2 Null and Alternative Hypotheses – Introduction to Statistics
September 1, 2022 - 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. State the null and alternative hypotheses. ... In a hypothesis test, sample data is evaluated in order to arrive at a decision about some type of claim.
Indeed
indeed.com › career guide › career development › what is an alternative hypothesis? (definition and examples)
What Is an Alternative Hypothesis? (Definition and Examples) | Indeed.com
August 16, 2024 - For instance, you can have a null hypothesis that says the melting point of aluminum is equal to 1,221 degrees, and your alternative hypothesis may say the melting point is greater than 1,221 degrees. Related: What Is a Null Hypothesis? (Definitions, Examples and FAQs)
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)
Alternative Hypothesis · Rejection Region · Ha: p > p0 · z ≥zα (upper-tailed) Ha: p < p0 · z ≤–zα (lower-tailed) Ha: p ≠ p0 · either z ≥zα/2 · or z ≤–zα/2 (two-tailed) These test procedures are valid provided that np0 ≥10 and · n(1 – p0) ≥10. 45 · Example ·
Laerd Statistics
statistics.laerd.com › statistical-guides › hypothesis-testing-3.php
Hypothesis Testing - Significance levels and rejecting or accepting the null hypothesis
For example, the two different teaching methods did not result in different exam performances (i.e., zero difference). Another example might be that there is no relationship between anxiety and athletic performance (i.e., the slope is zero). The alternative hypothesis states the opposite and ...
Wikipedia
en.wikipedia.org › wiki › Alternative_hypothesis
Alternative hypothesis - Wikipedia
October 6, 2025 - Namely, there is sufficient evidence against null hypothesis to demonstrate that the alternative hypothesis is true. One example is where water quality in a stream has been observed over many years, and a test is made of the null hypothesis that "there is no change in quality between the first and second halves of the data", against the alternative hypothesis that "the quality is poorer in the second half of the record".
Statlect
statlect.com › glossary › alternative-hypothesis
Alternative hypothesis | Explanation and examples
The choice between a one-tailed or a two-tailed test needs to be done in such a way that the interpretation of a rejection is always coherent with the alternative hypothesis. ... Example As in the previous example, consider a test about the mean of a normal distribution, where we test .
Study.com
study.com › skill › learn › how-to-write-an-alternative-hypothesis-for-a-given-situation-explanation.html
What is an Alternative Hypothesis For a Given Situation? | Statistics and Probability | Study.com
The alternative hypothesis claims that the company will have at least as many incidents next year as they did this year. Get access to thousands of practice questions and explanations! ... The Kite Runner by Khaled Hosseini: Ch. 7 | Summary &... The Kite Runner by Khaled Hosseini | Overview & Symbols · Differentiated Instruction in Math | Strategies & Examples
Omniconvert
omniconvert.com › home
Null and Alternative Hypothesis in A/B Testing
January 14, 2025 - In terms of A/B testing, the alternative hypothesis suggests that there is a difference between the control and variant groups. For example, during an experiment, your null hypothesis might state that changing the color of the website's CTA button from green to blue will not lead to any notable difference in conversion rates between the control and variant groups.
Quora
quora.com › What-are-some-examples-of-null-hypothesis-and-its-corresponding-alternative-hypothesis
What are some examples of null hypothesis and its corresponding alternative hypothesis? - Quora
Answer (1 of 3): These are statistical terms and are used only for statistical analysis. In statistics there is the population and there are the samples. The population is an idealized group of every example in every place through all of time. Say we are going to compare healing times of intrame...
Statistics LibreTexts
stats.libretexts.org › campus bookshelves › los angeles city college › introductory statistics › 9: hypothesis testing with one sample
9.2: Null and Alternative Hypotheses - Statistics LibreTexts
July 29, 2023 - The actual test begins by considering two hypotheses. They are called the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints. Since the null and alternative …
Quora
quora.com › How-do-you-write-a-null-and-alternative-hypothesis
How to write a null and alternative hypothesis - Quora
Answer (1 of 7): Null hypothesis & alternative hypothesis are used in hypothesis testing to check if an idea is true or not. Null hypothesis represents No change/the status quo, while alternative hypothesis represents change/challenges the status quo. Example 1 (One-tailed test), you want to kn...
Encyclopedia Britannica
britannica.com › science › alternative-hypothesis
Alternative hypothesis | statistics | Britannica
September 26, 2025 - Other articles where alternative hypothesis is discussed: statistics: Hypothesis testing: An alternative hypothesis (denoted Ha), which is the opposite of what is stated in the null hypothesis, is then defined. The hypothesis-testing procedure involves using sample data to determine whether or not H0 can be rejected.
BYJUS
byjus.com › maths › alternative-hypothesis
Difference Between Null and Alternative Hypothesis
August 28, 2019 - To check the water quality of a river for one year, the researchers are doing the observation. As per the null hypothesis, there is no change in water quality in the first half of the year as compared to the second half. But in the alternative hypothesis, the quality of water is poor in the second half when observed...
Formpl
formpl.us › blog › alternative-null-hypothesis
Alternative vs Null Hypothesis: Pros, Cons, Uses & Examples
November 22, 2021 - So if according to a null hypothesis something is correct to an alternate hypothesis that same thing will be incorrect. For example, let’s assume that you develop a null hypothesis that states “I”m going to be $500 richer” the alternate hypothesis will be “I’m going to get $500 or be richer”
Texas Gateway
texasgateway.org › resource › 91-null-and-alternative-hypotheses
9.1 Null and Alternative Hypotheses | Texas Gateway
This practice is acceptable because we only make the decision to reject or not reject the null hypothesis. H0: No more than 30 percent of the registered voters in Santa Clara County voted in the primary election. p ≤ 30 Ha: More than 30 percent of the registered voters in Santa Clara County voted in the primary election. p > 30 ... A medical trial is conducted to test whether or not a new medicine reduces cholesterol by 25 percent. State the null and alternative hypotheses.