Edit, I'm adding the rest of questions, maybe that'll help provide some context, although all questions revolve around the one originally shown part a. Answer from Nova_Katamaru_Kat on reddit.com
Lumen Learning
courses.lumenlearning.com › introstats1 › chapter › null-and-alternative-hypotheses
Null and Alternative Hypotheses | Introduction to Statistics
The null is not rejected unless the hypothesis test shows otherwise. The null statement must always contain some form of equality (=, ≤ or ≥) Always write the alternative hypothesis, typically denoted with Ha or H1, using less than, greater than, or not equals symbols, i.e., (≠, >, or <).
Scribbr
scribbr.com › home › null and alternative hypotheses | definitions & examples
Null and Alternative Hypotheses | Definitions & Examples
January 24, 2025 - The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:
how do I determine the null and alternative hypothesis with this given information?
Edit, I'm adding the rest of questions, maybe that'll help provide some context, although all questions revolve around the one originally shown part a. More on reddit.com
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March 10, 2023How to write multiple hypotheses?
The final null hypothesis (H3: 0) is that gamified VR did not positively affect the mental health of participants. The final alternate hypothesis (H3: 1) is that gamified VR had a positive impact on the mental health of participants. ... I'm wondering the same thing because i've run into the same hitch with my minor research project. I've multiple hypothesis and ... More on researchgate.net
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August 12, 2020Null 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, 2021How do you write null and alternative hypothesis for mediation and moderation effect?
Null hypothesis have become old fashion now days. You can use alternative hypothesis by following main ketwords in hypothesis for mediator (Intervening, Via, indirect effect), and Moderator (High vs low, strong vs weak). ... @Naveed Ahmad: thanks for the suggestion, but my university requires me to write ... More on researchgate.net
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April 27, 2017What 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 and Alternative Hypotheses | Definitions & Examples
What’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 and Alternative Hypotheses | Definitions & 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.
scribbr.com
scribbr.com › home › null and alternative hypotheses | definitions & examples
Null and Alternative Hypotheses | Definitions & Examples
Videos
Examples of null and alternative hypotheses (video)
06:15
Writing Null and Alternative Hypotheses - YouTube
14:38
Writing the Null and Alternate Hypothesis in Statistics - YouTube
07:26
Setting Up the Null and the Alternative Hypotheses - YouTube
03:49
Null and alternative hypotheses with Lindsey Leach - YouTube
06:52
Hypothesis Testing - Null and Alternative Hypotheses - YouTube
Texas Gateway
texasgateway.org › resource › 91-null-and-alternative-hypotheses
9.1 Null and Alternative Hypotheses | Texas Gateway
The choice of symbol depends on the wording of the hypothesis test. However, be aware that many researchers use = in the null hypothesis, even with > or < as the symbol in the alternative hypothesis. This practice is acceptable because we only make the decision to reject or not reject the null ...
Statistics LibreTexts
stats.libretexts.org › campus bookshelves › las positas college › math 40: statistics and probability › 8: hypothesis testing with one sample › 8.1: steps in hypothesis testing
8.1.1: Null and Alternative Hypotheses - Statistics LibreTexts
August 8, 2020 - After you have determined which hypothesis the sample supports, you make a decision. There are two options for a decision. They are "reject \(H_0\)" if the sample information favors the alternative hypothesis or "do not reject \(H_0\)" or "decline to reject \(H_0\)" if the sample information is insufficient to reject the null hypothesis.
National University
resources.nu.edu › statsresources › hypothesis
Null & Alternative Hypotheses - Statistics Resources - LibGuides at National University
October 27, 2025 - This is your answer to your research question. ... Null Hypothesis: H0: There is no difference in the salary of factory workers based on gender. Alternative Hypothesis: Ha: Male factory workers have a higher salary than female factory workers. Null Hypothesis: H0: There is no relationship between ...
Reddit
reddit.com › r/askmath › how do i determine the null and alternative hypothesis with this given information?
r/askmath on Reddit: how do I determine the null and alternative hypothesis with this given information?
March 10, 2023 - If what I said before is reasonable then the alternative hypothesis should be that there is a significant difference depending on the level of education. More replies ... This is not means. It 's about frequencies. ... Then I'd have no idea where to begin, question 1, 2, and 4 have all been about the means so I'd be lost again ... This is chi-square test of independence. Read up on it. The null H would be: there is no relationship between a person's educational level and their preferred source of news.
Minitab
support.minitab.com › en-us › minitab › help-and-how-to › statistics › basic-statistics › supporting-topics › basics › null-and-alternative-hypotheses
About the null and alternative hypotheses - Minitab
You can specify the direction to be either greater than or less than the hypothesized value. A one-sided test has greater power than a two-sided test, but it cannot detect whether the population parameter differs in the opposite direction. ... A researcher has results for a sample of students who took a national exam at a high school. The researcher wants to know if the scores at that school differ from the national average of 850. A two-sided alternative hypothesis (also known as a nondirectional hypothesis) is appropriate because the researcher is interested in determining whether the scores are either less than or greater than the national average.
Penn State University
online.stat.psu.edu › stat200 › lesson › 5 › 5.2
5.2 - Writing Hypotheses | STAT 200
For each test you will have a null hypothesis (\(H_0\)) and an alternative hypothesis (\(H_a\)).
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In my experience stating null and alternate hypotheses does not extend far outside of the classroom. The idea is to state your alternate hypotheses, but then your experimental approach should be to test the null hypotheses. The more simple the hypotheses the better. Maybe something like this:
We will explore three hypotheses in this study:
1. Gamified VR will increase adherence to exercise
2. Gamified VR will result in lower rates of perceived exertion
3. Gamified VR will have a positive impact on mental health
I feel that the brief explanations/details you provide at the end of each hypothesis would not normally be included in the hypothesis itself, but rather should be elaborated on in the proceeding text.
Good luck!
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Thanks Paul Hafen ! Thats helpful :)
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 - After you have determined which hypothesis the sample supports, you make a decision. There are two options for a decision. They are "reject \(H_0\)" if the sample information favors the alternative hypothesis or "do not reject \(H_0\)" or "decline to reject \(H_0\)" if the sample information is insufficient to reject 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.
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.
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Null hypothesis have become old fashion now days. You can use alternative hypothesis by following main ketwords in hypothesis for mediator (Intervening, Via, indirect effect), and Moderator (High vs low, strong vs weak).
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Thank you very much Richard for your answer.
i just want to confirm one thing regarding the proposition 1. if somehow null hypothesis ("The impact of regional conditions on entrepreneurial intentions is unaffected by attitude [etc.]") become the accepted one, it does not imply that regional conditions have "direct" effect on entrepreneurial intentions, does it?
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 ... 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 ...
Reddit
reddit.com › r/askstatistics › how do i frame null hypothesis and alternative hypothesis here?
r/AskStatistics on Reddit: How do I frame null hypothesis and alternative hypothesis here?
December 8, 2020 -
Hey guys,
I've just started self learning hypothesis testing and am getting confused about framing the null hypothesis and alternative hypothesis in this question.
Usually every question I came accross made more sense and had a defining point at which it should be considered high satisfaction and below which it would be low.
Best I could come up with is (H0 : score > 30) but just assuming everything above mean is high feels wrong cause there's nothing specified in the question.
So in case I come accross questions like this how to I approach framing the null hypothesis and alternative hypothesis?
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In words: H0: Group A and Group B have equal life satisfaction HA: Group A and Group B have different life satisfaction Which I would translate as H0: meanA = meanB HA: meanB =/= meanB So you'd have to find the CI for each group and see if they overlap. It's been a while (25 years) since I've done this kind of thing, so I might be wrong, but I don't think I am. edit: typo in HA!
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Hypotheses are about population quantities. "score>30" is not of that form Null hypotheses should normally include an equality (otherwise you run into some issues with logic/interpretation) "had a defining point at which it should be considered high satisfaction and below which it would be low" is talking about a one sample test. This is clearly some kind of location comparison on two samples. However the question does not suggest which specific sort of population comparison might be of interest; you'd need to think about whether you are going to compare means or something else. Where does the question come from? [There's some issues with the question: "A research" is not correct English, "research" is effectively a mass noun, you can't enumerate it - either "Research was..." or "A piece of research was..." would work; "excel" should be capitalized; "justify with" is not correct English; the "with" could be omitted. There are some other issues as well, but those were the ones that bothered my enough to mention. The author needs a proofreader.]
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A rule of the thumb from a good advisor of mine was to set the Null-Hypothesis to the outcome you do not want to be true i.e. the outcome whose direct opposite you want to show.
Basic example: Suppose you have developed a new medical treatment and you want to show that it is indeed better than placebo. So you set Null-Hypothesis $H_0:=$new treament is equal or worse than placebo and Alternative Hypothesis $H_1:=$new treatment is better than placebo.
This because in the course of a statistical test you either reject the Null-Hypothesis (and favor the Alternative Hypothesis) or you cannot reject it. Since your "goal" is to reject the Null-Hypothesis you set it to the outcome you do not want to be true.
Side Note: I am aware that one should not set up a statistical test to twist it and break it until the Null-Hypothesis is rejected, the casual language was only used to make this rule easier to remember.
This also may be helpful: What is the meaning of p values and t values in statistical tests? and/or What is a good introduction to statistical hypothesis testing for computer scientists?
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If hypothesis B is the interesting hypothesis you can take not-B as the null hypothesis and control, under the null, the probability of the type I error for wrongly rejecting not-B at level $\alpha$. Rejecting not-B is then interpreted as evidence in favor of B because we control the type I error, hence it is unlikely that not-B is true. Confused ... ?
Take the example of treatment vs. no treatment in two groups from a population. The interesting hypothesis is that treatment has an effect, that is, there is a difference between the treated group and the untreated group due to the treatment. The null hypothesis is that there is no difference, and we control the probability of wrongly rejecting this hypothesis. Thus we control the probability of wrongly concluding that there is a treatment effect when there is no treatment effect. The type II error is the probability of wrongly accepting the null when there is a treatment effect.
The formulation above is based on the Neyman-Pearson framework for statistical testing, where statistical testing is seen as a decision problem between to cases, the null and the alternative. The level $\alpha$ is the fraction of times we make a type I error if we (independently) repeat the test. In this framework there is really not any formal distinction between the null and the alternative. If we interchange the null and the alternative, we interchange the probability of type I and type II errors. We did not, however, control the type II error probability above (it depends upon how big the treatment effect is), and due to this asymmetry, we may prefer to say that we fail to reject the null hypothesis (instead of that we accept the null hypothesis). Thus we should be careful about concluding that the null hypothesis is true just because we can't reject it.
In a Fisherian significance testing framework there is really only a null hypothesis and one computes, under the null, a $p$-value for the observed data. Smaller $p$-values are interpreted as stronger evidence against the null. Here the null hypothesis is definitely not-B (no effect of treatment) and the $p$-value is interpreted as the amount of evidence against the null. With a small $p$-value we can confidently reject the null, that there is no treatment effect, and conclude that there is a treatment effect. In this framework we can only reject or not reject (never accept) the null, and it is all about falsifying the null. Note that the $p$-value does not need to be justified by an (imaginary) repeated number of decisions.
Neither framework is without problems, and the terminology is often mixed up. I can recommend the book Statistical evidence: a likelihood paradigm by Richard M. Royall for a clear treatment of the different concepts.
Medium
medium.com › @andersongimino › differences-between-the-null-and-alternative-hypotheses-6b2e794543f6
Differences between the null and alternative hypotheses | by Anderson Gimino | Medium
July 14, 2023 - The company implements a new sales training program for their employees. They ask you to evaluate the effectiveness of the program. Your null hypothesis (H0): the program had no effect on sales revenue. Your alternative hypothesis (Ha): the program increased sales revenue.
Psychstat
advstats.psychstat.org › book › hypothesis › index.php
Null hypothesis testing -- Advanced Statistics using R
In R, the function t.test() can be used to conduct a $t$ test. The following code conducts the Welch's $t$ test. Note that alternative = "greater" sets the alternative hypothesis. The other options include two.sided and less.
Tallahassee State College
tsc.fl.edu › media › divisions › learning-commons › resources-by-subject › math › statistics › Claim-and-Hypothesis.pdf pdf
Identifying the Claim and Setting up Hypothesis for µ or π STA 2023 & 2122
either more or less than 50%. Having this in mind the Null and Alternative Hypothesis looks like: H0: π =.5 (This will be the claim). H1: π ≠.5 · Example 4: An electrical company claimed that less than 2% of · the parts which they supplied on a government contract are defective. A sample of 642 parts was tested, and 17 did not meet the specifications. Can we accept the ... They want to test the proportion of the parts that do not meet the specifications.