Open Textbook BC
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Understanding Null Hypothesis Testing – Research Methods in Psychology – 2nd Canadian Edition
October 13, 2015 - The p value is one of the most misunderstood quantities in psychological research (Cohen, 1994)[1]. Even professional researchers misinterpret it, and it is not unusual for such misinterpretations to appear in statistics textbooks! The most common misinterpretation is that the p value is the probability that the null hypothesis is true—that the sample result occurred by chance. For example, a misguided researcher might say that because the p value is .02, there is only a 2% chance that the result is due to chance and a 98% chance that it reflects a real relationship in the population.
Laerd Statistics
statistics.laerd.com › statistical-guides › hypothesis-testing-3.php
Hypothesis Testing - Significance levels and rejecting or accepting the null hypothesis
The null hypothesis is essentially the "devil's advocate" position. That is, it assumes that whatever you are trying to prove did not happen (hint: it usually states that something equals zero). For example, the two different teaching methods did not result in different exam performances (i.e., ...
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, 2021ELI5 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".
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November 23, 2021Null hypothesis?!
The null hypothesis just says whatever you think is causing something isn’t the cause. For example, if you think drinking coffee causes a person to stay awake for longer periods of time, then the null hypothesis will state there will be no change in length of time people stay awake when drinking coffee. The alternative hypothesis is the hypothesis that coffee does cause one to stay awake longer. We can relate this to p values, which usually we have a p value of <0.05. What this means is that if we were to run some experiment with coffee and sleep. The data we would get would have a less than a 5% chance of being due to randomness. We reject the null hypothesis when the chance of the getting data this extreme is less than 5%. The alternative hypothesis is supported at p <0.05. More on reddit.com
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November 23, 2020ELI5: NULL Hypothesis
The null hypothesis assumes that there is no relationship between the variables you want to test and it exists for the sake of demonstrating statistical significance of data.
Lets say you have a hypothesis that X happens when you do Y. You preform an experiment where you do Y, and lo and behold X happens. The evidence from your experiment supports your hypothesis, but how do you know it wasn't a fluke? Maybe X and Y have nothing to do with one another, and they just happened to coincide this time?
This is where the null hypothesis comes in. It assumes that X is just as likely to happen, regardless of whether or not you do Y. A central task in proving your hypothesis is rejecting the null hypothesis. This is done through statistical analysis of your data to determine the likelihood of obtaining your results should the null hypothesis be true.
If that likelihood is low enough (usually <1-5%), you can say that your results are "statistically significant," or in other words your results are not a fluke and are therefore indicative of the relationship between X and Y.
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December 15, 2013Videos
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Null Hypothesis | Definition & Examples - Video | Study.com
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Hypothesis vs Null Hypothesis and Statistics - YouTube
Examples of null and alternative hypotheses (video)
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The Null Hypothesis and Alternative Hypothesis in Statistics Testing ...
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Hypothesis Testing and The Null Hypothesis - YouTube
ThoughtCo
thoughtco.com › null-hypothesis-examples-609097
How to Formulate a Null Hypothesis (With Examples)
May 7, 2024 - For the first item in the table above, for example, an alternative hypothesis might be "Age does have an effect on mathematical ability." In hypothesis testing, the null hypothesis assumes no relationship between two variables, providing a baseline for statistical analysis. Rejecting the null hypothesis suggests there is evidence of a relationship between variables. By formulating a null hypothesis, researchers can systematically test assumptions and draw more reliable conclusions from their experiments.
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 ...
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...
iSixSigma
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Null Hypothesis vs. Hypothesis: What's the Difference? - isixsigma.com
... “There is no relationship ... and other financial markets. An example of a null hypothesis: The mean annual return of a stock option is 3%....
Published February 4, 2025
Helpful Professor
helpfulprofessor.com › home › 15 null hypothesis examples
15 Null Hypothesis Examples (2025)
August 26, 2023 - Clinical Trials: Null hypothesis is used extensively in medical and pharmaceutical research. For example, when testing a new drug’s effectiveness, the null hypothesis might state that the drug has no effect on the disease.
Researcher.Life
researcher.life › home › what is a research hypothesis: how to write it, types, and examples
What is a research hypothesis: How to write it, types, and examples | Researcher.Life
February 6, 2025 - This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states. Example: “The newly identified virus is not zoonotic.”
Optimizely
optimizely.com › optimization-glossary › null-hypothesis
What is a null hypothesis? - Optimizely
June 10, 2025 - It involves failing to reject a false null hypothesis and missing real effects. Balancing these error types is crucial for reliable conclusions. ... Identify the research question: Define the specific question you want to answer through your research or experiment.
Investopedia
investopedia.com › terms › n › null_hypothesis.asp
Null Hypothesis: What Is It and How Is It Used in Investing?
May 8, 2025 - Depending on the question, the null may be identified differently. For example, if the question is simply whether an effect exists (e.g., does X influence Y?), the null hypothesis could be H0: X = 0.
Statistics By Jim
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Null Hypothesis: Definition, Rejecting & Examples - Statistics By Jim
November 7, 2022 - In the case of a one-sided hypothesis test, the null still contains an equal sign but it’s “greater than or equal to” or “less than or equal to.” If you wanted to translate the null hypothesis from its native mathematical expression, you could use the expression “there is no effect.” But the mathematical form more specifically states what it’s testing. It’s the alternative hypothesis that typically contains does not equal. There are some exceptions. For example, in an equivalence test where the researchers want to show that two things are equal, the null hypothesis states that they’re not equal.
Scribbr
scribbr.com › home › null and alternative hypotheses | definitions & examples
Null & Alternative Hypotheses | Definitions, Templates & Examples
January 24, 2025 - Rewrite and paraphrase texts instantly with our AI-powered paraphrasing tool. ... Eliminate grammar errors and improve your writing with our free AI-powered grammar checker. ... Published on May 6, 2022 by Shaun Turney. Revised on January 24, 2025. The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test: Null hypothesis (H0): There’s no effect in the population.
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. ... Next: One-Tail vs. Two-Tail >> ... Doctoral Center Institutional Review Board Advanced Research Center Institutional Repository NU Commons
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.
Corporate Finance Institute
corporatefinanceinstitute.com › home › resources › null hypothesis
Null Hypothesis - Overview, How It Works, Example
November 21, 2023 - For example, a null hypothesis statement can be “the rate of plant growth is not affected by sunlight.” It can be tested by measuring the growth of plants in the presence of sunlight and comparing this with the growth of plants in the absence ...
Lumen Learning
courses.lumenlearning.com › introstats1 › chapter › null-and-alternative-hypotheses
Null and Alternative Hypotheses | Introduction to Statistics
This practice is acceptable because we only make the decision to reject or not reject the null hypothesis. H0: No more than 30% of the registered voters in Santa Clara County voted in the primary election. p ≤ 30 · Ha: More than 30% 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%. State the null and alternative hypotheses.
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 ... They want to test what proportion of the parts do not meet the specifications. Since they claim · that the proportion is less than 2%, the symbol for the Alternative Hypothesis will be <. As is the
Statistics Solutions
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Constructing Hypotheses in Quantitative Research - Statistics Solutions
May 16, 2025 - For example, if studying how mentoring affects first-generation students’ intention to stay at university, the research question could be: “Does the presence of a mentoring relationship influence first-generation students’ intentions to remain at their university?” · Though this statement outlines the construct and variables, it doesn’t specify what we’re testing. We use the hypotheses to make this clear. Specifically, we create null and alternate hypotheses to indicate exactly what we intend to test. The null hypothesis suggests no observable difference or relationship, while the alternate hypothesis suggests there is one.
Journal of Cardiothoracic and Vascular Anesthesia
jcvaonline.com › article › S1053-0770(23)00117-9 › fulltext
The Art of the Null Hypothesis—Considerations for Study Design and Scientific Reporting - Journal of Cardiothoracic and Vascular Anesthesia
February 21, 2023 - He et al. again excellently demonstrated this concept, stating in their discussion, “an anesthetic maintenance regimen with a volatile anesthetic was not statistically superior to propofol-based TIVA regarding the occurrence of pulmonary complications.”4 This statement clearly summarizes the study that the null hypothesis could not be rejected while leaving open the possibility that true differences between groups may exist but could not be detected. Research studies frequently have numerous outcomes, and standard null hypothesis testing for multiple endpoints requires modifications.