null hypothesis xkcd

2020: Negative Results

Explanation [ edit ].

Recently, scientists have begun encouraging each other to publish negative results, where a study failed to find the intended effect, as a way of counteracting publication bias (where only interesting positive results get published), which results in false-positive results being published while negative results are not.

Cueball misinterprets the "push to publish negative results" as meaning that he should always attempt to publish the fact that he failed to find evidence of an effect, even when he didn't even try, spending his time playing a video game instead. This plays on the unspoken assumption that scientists would only choose to submit (and journals would only accept) negative results where a study was designed and executed well enough that it should have shown an effect or at least demonstrated evidence of some kind.

Besides personal preferences, The Legend of Zelda: Breath of the Wild , the most recent Legend of Zelda game at the time of publication, was likely chosen for its notable length, Nintendo nerd cred, and a relevance to Nature magazine’s subject. The average time to beat 100% of the content is over 175 hours .

The title text references the practice of "pre-registration" of a study, which is one means to prevent publication bias: details of a planned study are registered with an organization before the study is conducted, so that a null result or a change in methodology cannot be hidden. The title text may be a play on words, mixing this up with registering (or booking) travel. On the other hand, it may just be playing on the absurdity of pre-registering a simple trip to the beach with a registry for scientific studies.

Transcript [ edit ]

The idea of publishing "failed" investigations arose out of the demand to punish all of the results from medical trials. Then there was the realisation that more than one team may have had the same hypotheses, got funding, investigated and not published the proof that they were wrong. So the idea that a A =/= B is still a valuable finding to be reported has come about. There may seem to be lots being published due to years of keeping silent about such results. RIIW - Ponder it ( talk ) 20:13, 16 July 2018 (UTC)

There can be several reasons, why a study has a negative outcome or is delayed. Most of those, especially from smaller studies, are not directly related to the matter being investigated, but more to study design, analysis tools or organizational issues. It is much easier to get a wrong or no result than the correct one. The best solution is to somewhere publish these failed experiments and describe the circumstances and reasons so that it can be judged by a third party (even if that is an embarassment for the scientists in an institution). But if you report that you have started a study, and the reasons are rather mundane as in the case within the comic, what should you report? The truth? Should you lie? Sebastian -- 172.68.110.58 05:38, 17 July 2018 (UTC)

I don't think the "null hypothesis" is a reference to 892: Null Hypothesis , as the explanation currently says. Sure, the comic doesn't mention any particular null hypothesis, but it does say "...the null hypothesis in any research areas", which might equally have been phrased "the null hypothesis of any research areas". In which case he's just saying that he hasn't rejected anyone's null hypothesis lately, not that (as in the earlier comic) he's treating "the null hypothesis" as a single, refutable-once-and-for-all thing. -- Peregrine ( talk ) 08:32, 17 July 2018 (UTC)

The plural in research areas did not seem correct until I read [ [1] ]a quote from Michael Swan (Practical English Usage, Oxford University Press, 1995):

Connection to Significant [ edit ]

There was a previous comic about publication bias, 882: Significant . 162.158.89.7 10:02, 18 July 2018 (UTC)

Comics from 2018
Comics from July
Monday comics
Comics featuring Cueball
Video games
Scientific research

Null Hypothesis

Hell, my eighth grade science class managed to conclusively reject it just based on a classroom experiment. It's pretty sad to hear about million-dollar research teams who can't even manage that.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 13: Inferential Statistics

Understanding Null Hypothesis Testing

Learning Objectives

Explain the purpose of null hypothesis testing, including the role of sampling error.
Describe the basic logic of null hypothesis testing.
Describe the role of relationship strength and sample size in determining statistical significance and make reasonable judgments about statistical significance based on these two factors.

The Purpose of Null Hypothesis Testing

As we have seen, psychological research typically involves measuring one or more variables for a sample and computing descriptive statistics for that sample. In general, however, the researcher’s goal is not to draw conclusions about that sample but to draw conclusions about the population that the sample was selected from. Thus researchers must use sample statistics to draw conclusions about the corresponding values in the population. These corresponding values in the population are called parameters . Imagine, for example, that a researcher measures the number of depressive symptoms exhibited by each of 50 clinically depressed adults and computes the mean number of symptoms. The researcher probably wants to use this sample statistic (the mean number of symptoms for the sample) to draw conclusions about the corresponding population parameter (the mean number of symptoms for clinically depressed adults).

Unfortunately, sample statistics are not perfect estimates of their corresponding population parameters. This is because there is a certain amount of random variability in any statistic from sample to sample. The mean number of depressive symptoms might be 8.73 in one sample of clinically depressed adults, 6.45 in a second sample, and 9.44 in a third—even though these samples are selected randomly from the same population. Similarly, the correlation (Pearson’s r ) between two variables might be +.24 in one sample, −.04 in a second sample, and +.15 in a third—again, even though these samples are selected randomly from the same population. This random variability in a statistic from sample to sample is called sampling error . (Note that the term error here refers to random variability and does not imply that anyone has made a mistake. No one “commits a sampling error.”)

One implication of this is that when there is a statistical relationship in a sample, it is not always clear that there is a statistical relationship in the population. A small difference between two group means in a sample might indicate that there is a small difference between the two group means in the population. But it could also be that there is no difference between the means in the population and that the difference in the sample is just a matter of sampling error. Similarly, a Pearson’s r value of −.29 in a sample might mean that there is a negative relationship in the population. But it could also be that there is no relationship in the population and that the relationship in the sample is just a matter of sampling error.

In fact, any statistical relationship in a sample can be interpreted in two ways:

There is a relationship in the population, and the relationship in the sample reflects this.
There is no relationship in the population, and the relationship in the sample reflects only sampling error.

The purpose of null hypothesis testing is simply to help researchers decide between these two interpretations.

The Logic of Null Hypothesis Testing

Null hypothesis testing is a formal approach to deciding between two interpretations of a statistical relationship in a sample. One interpretation is called the null hypothesis (often symbolized H 0 and read as “H-naught”). This is the idea that there is no relationship in the population and that the relationship in the sample reflects only sampling error. Informally, the null hypothesis is that the sample relationship “occurred by chance.” The other interpretation is called the alternative hypothesis (often symbolized as H 1 ). This is the idea that there is a relationship in the population and that the relationship in the sample reflects this relationship in the population.

Again, every statistical relationship in a sample can be interpreted in either of these two ways: It might have occurred by chance, or it might reflect a relationship in the population. So researchers need a way to decide between them. Although there are many specific null hypothesis testing techniques, they are all based on the same general logic. The steps are as follows:

Assume for the moment that the null hypothesis is true. There is no relationship between the variables in the population.
Determine how likely the sample relationship would be if the null hypothesis were true.
If the sample relationship would be extremely unlikely, then reject the null hypothesis in favour of the alternative hypothesis. If it would not be extremely unlikely, then retain the null hypothesis .

Following this logic, we can begin to understand why Mehl and his colleagues concluded that there is no difference in talkativeness between women and men in the population. In essence, they asked the following question: “If there were no difference in the population, how likely is it that we would find a small difference of d = 0.06 in our sample?” Their answer to this question was that this sample relationship would be fairly likely if the null hypothesis were true. Therefore, they retained the null hypothesis—concluding that there is no evidence of a sex difference in the population. We can also see why Kanner and his colleagues concluded that there is a correlation between hassles and symptoms in the population. They asked, “If the null hypothesis were true, how likely is it that we would find a strong correlation of +.60 in our sample?” Their answer to this question was that this sample relationship would be fairly unlikely if the null hypothesis were true. Therefore, they rejected the null hypothesis in favour of the alternative hypothesis—concluding that there is a positive correlation between these variables in the population.

A crucial step in null hypothesis testing is finding the likelihood of the sample result if the null hypothesis were true. This probability is called the p value . A low p value means that the sample result would be unlikely if the null hypothesis were true and leads to the rejection of the null hypothesis. A high p value means that the sample result would be likely if the null hypothesis were true and leads to the retention of the null hypothesis. But how low must the p value be before the sample result is considered unlikely enough to reject the null hypothesis? In null hypothesis testing, this criterion is called α (alpha) and is almost always set to .05. If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected. When this happens, the result is said to be statistically significant . If there is greater than a 5% chance of a result as extreme as the sample result when the null hypothesis is true, then the null hypothesis is retained. This does not necessarily mean that the researcher accepts the null hypothesis as true—only that there is not currently enough evidence to conclude that it is true. Researchers often use the expression “fail to reject the null hypothesis” rather than “retain the null hypothesis,” but they never use the expression “accept the null hypothesis.”

The Misunderstood p Value

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. But this is incorrect . The p value is really the probability of a result at least as extreme as the sample result if the null hypothesis were true. So a p value of .02 means that if the null hypothesis were true, a sample result this extreme would occur only 2% of the time.

You can avoid this misunderstanding by remembering that the p value is not the probability that any particular hypothesis is true or false. Instead, it is the probability of obtaining the sample result if the null hypothesis were true.

Role of Sample Size and Relationship Strength

Recall that null hypothesis testing involves answering the question, “If the null hypothesis were true, what is the probability of a sample result as extreme as this one?” In other words, “What is the p value?” It can be helpful to see that the answer to this question depends on just two considerations: the strength of the relationship and the size of the sample. Specifically, the stronger the sample relationship and the larger the sample, the less likely the result would be if the null hypothesis were true. That is, the lower the p value. This should make sense. Imagine a study in which a sample of 500 women is compared with a sample of 500 men in terms of some psychological characteristic, and Cohen’s d is a strong 0.50. If there were really no sex difference in the population, then a result this strong based on such a large sample should seem highly unlikely. Now imagine a similar study in which a sample of three women is compared with a sample of three men, and Cohen’s d is a weak 0.10. If there were no sex difference in the population, then a relationship this weak based on such a small sample should seem likely. And this is precisely why the null hypothesis would be rejected in the first example and retained in the second.

Of course, sometimes the result can be weak and the sample large, or the result can be strong and the sample small. In these cases, the two considerations trade off against each other so that a weak result can be statistically significant if the sample is large enough and a strong relationship can be statistically significant even if the sample is small. Table 13.1 shows roughly how relationship strength and sample size combine to determine whether a sample result is statistically significant. The columns of the table represent the three levels of relationship strength: weak, medium, and strong. The rows represent four sample sizes that can be considered small, medium, large, and extra large in the context of psychological research. Thus each cell in the table represents a combination of relationship strength and sample size. If a cell contains the word Yes , then this combination would be statistically significant for both Cohen’s d and Pearson’s r . If it contains the word No , then it would not be statistically significant for either. There is one cell where the decision for d and r would be different and another where it might be different depending on some additional considerations, which are discussed in Section 13.2 “Some Basic Null Hypothesis Tests”

Although Table 13.1 provides only a rough guideline, it shows very clearly that weak relationships based on medium or small samples are never statistically significant and that strong relationships based on medium or larger samples are always statistically significant. If you keep this lesson in mind, you will often know whether a result is statistically significant based on the descriptive statistics alone. It is extremely useful to be able to develop this kind of intuitive judgment. One reason is that it allows you to develop expectations about how your formal null hypothesis tests are going to come out, which in turn allows you to detect problems in your analyses. For example, if your sample relationship is strong and your sample is medium, then you would expect to reject the null hypothesis. If for some reason your formal null hypothesis test indicates otherwise, then you need to double-check your computations and interpretations. A second reason is that the ability to make this kind of intuitive judgment is an indication that you understand the basic logic of this approach in addition to being able to do the computations.

Statistical Significance Versus Practical Significance

Table 13.1 illustrates another extremely important point. A statistically significant result is not necessarily a strong one. Even a very weak result can be statistically significant if it is based on a large enough sample. This is closely related to Janet Shibley Hyde’s argument about sex differences (Hyde, 2007) [2] . The differences between women and men in mathematical problem solving and leadership ability are statistically significant. But the word significant can cause people to interpret these differences as strong and important—perhaps even important enough to influence the college courses they take or even who they vote for. As we have seen, however, these statistically significant differences are actually quite weak—perhaps even “trivial.”

This is why it is important to distinguish between the statistical significance of a result and the practical significance of that result. Practical significance refers to the importance or usefulness of the result in some real-world context. Many sex differences are statistically significant—and may even be interesting for purely scientific reasons—but they are not practically significant. In clinical practice, this same concept is often referred to as “clinical significance.” For example, a study on a new treatment for social phobia might show that it produces a statistically significant positive effect. Yet this effect still might not be strong enough to justify the time, effort, and other costs of putting it into practice—especially if easier and cheaper treatments that work almost as well already exist. Although statistically significant, this result would be said to lack practical or clinical significance.

Key Takeaways

Null hypothesis testing is a formal approach to deciding whether a statistical relationship in a sample reflects a real relationship in the population or is just due to chance.
The logic of null hypothesis testing involves assuming that the null hypothesis is true, finding how likely the sample result would be if this assumption were correct, and then making a decision. If the sample result would be unlikely if the null hypothesis were true, then it is rejected in favour of the alternative hypothesis. If it would not be unlikely, then the null hypothesis is retained.
The probability of obtaining the sample result if the null hypothesis were true (the p value) is based on two considerations: relationship strength and sample size. Reasonable judgments about whether a sample relationship is statistically significant can often be made by quickly considering these two factors.
Statistical significance is not the same as relationship strength or importance. Even weak relationships can be statistically significant if the sample size is large enough. It is important to consider relationship strength and the practical significance of a result in addition to its statistical significance.
Discussion: Imagine a study showing that people who eat more broccoli tend to be happier. Explain for someone who knows nothing about statistics why the researchers would conduct a null hypothesis test.
The correlation between two variables is r = −.78 based on a sample size of 137.
The mean score on a psychological characteristic for women is 25 ( SD = 5) and the mean score for men is 24 ( SD = 5). There were 12 women and 10 men in this study.
In a memory experiment, the mean number of items recalled by the 40 participants in Condition A was 0.50 standard deviations greater than the mean number recalled by the 40 participants in Condition B.
In another memory experiment, the mean scores for participants in Condition A and Condition B came out exactly the same!
A student finds a correlation of r = .04 between the number of units the students in his research methods class are taking and the students’ level of stress.

Long Descriptions

“Null Hypothesis” long description: A comic depicting a man and a woman talking in the foreground. In the background is a child working at a desk. The man says to the woman, “I can’t believe schools are still teaching kids about the null hypothesis. I remember reading a big study that conclusively disproved it years ago.” [Return to “Null Hypothesis”]

“Conditional Risk” long description: A comic depicting two hikers beside a tree during a thunderstorm. A bolt of lightning goes “crack” in the dark sky as thunder booms. One of the hikers says, “Whoa! We should get inside!” The other hiker says, “It’s okay! Lightning only kills about 45 Americans a year, so the chances of dying are only one in 7,000,000. Let’s go on!” The comic’s caption says, “The annual death rate among people who know that statistic is one in six.” [Return to “Conditional Risk”]

Media Attributions

Null Hypothesis by XKCD CC BY-NC (Attribution NonCommercial)
Conditional Risk by XKCD CC BY-NC (Attribution NonCommercial)
Cohen, J. (1994). The world is round: p < .05. American Psychologist, 49 , 997–1003. ↵
Hyde, J. S. (2007). New directions in the study of gender similarities and differences. Current Directions in Psychological Science, 16 , 259–263. ↵

Values in a population that correspond to variables measured in a study.

The random variability in a statistic from sample to sample.

A formal approach to deciding between two interpretations of a statistical relationship in a sample.

The idea that there is no relationship in the population and that the relationship in the sample reflects only sampling error.

The idea that there is a relationship in the population and that the relationship in the sample reflects this relationship in the population.

When the relationship found in the sample would be extremely unlikely, the idea that the relationship occurred “by chance” is rejected.

When the relationship found in the sample is likely to have occurred by chance, the null hypothesis is not rejected.

The probability that, if the null hypothesis were true, the result found in the sample would occur.

How low the p value must be before the sample result is considered unlikely in null hypothesis testing.

When there is less than a 5% chance of a result as extreme as the sample result occurring and the null hypothesis is rejected.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

13.1 Understanding Null Hypothesis Testing

Learning objectives.

Explain the purpose of null hypothesis testing, including the role of sampling error.
Describe the basic logic of null hypothesis testing.
Describe the role of relationship strength and sample size in determining statistical significance and make reasonable judgments about statistical significance based on these two factors.

The Purpose of Null Hypothesis Testing

As we have seen, psychological research typically involves measuring one or more variables in a sample and computing descriptive statistics for that sample. In general, however, the researcher’s goal is not to draw conclusions about that sample but to draw conclusions about the population that the sample was selected from. Thus researchers must use sample statistics to draw conclusions about the corresponding values in the population. These corresponding values in the population are called parameters . Imagine, for example, that a researcher measures the number of depressive symptoms exhibited by each of 50 adults with clinical depression and computes the mean number of symptoms. The researcher probably wants to use this sample statistic (the mean number of symptoms for the sample) to draw conclusions about the corresponding population parameter (the mean number of symptoms for adults with clinical depression).

Unfortunately, sample statistics are not perfect estimates of their corresponding population parameters. This is because there is a certain amount of random variability in any statistic from sample to sample. The mean number of depressive symptoms might be 8.73 in one sample of adults with clinical depression, 6.45 in a second sample, and 9.44 in a third—even though these samples are selected randomly from the same population. Similarly, the correlation (Pearson’s r ) between two variables might be +.24 in one sample, −.04 in a second sample, and +.15 in a third—again, even though these samples are selected randomly from the same population. This random variability in a statistic from sample to sample is called sampling error . (Note that the term error here refers to random variability and does not imply that anyone has made a mistake. No one “commits a sampling error.”)

In fact, any statistical relationship in a sample can be interpreted in two ways:

There is a relationship in the population, and the relationship in the sample reflects this.
There is no relationship in the population, and the relationship in the sample reflects only sampling error.

The purpose of null hypothesis testing is simply to help researchers decide between these two interpretations.

The Logic of Null Hypothesis Testing

Assume for the moment that the null hypothesis is true. There is no relationship between the variables in the population.
Determine how likely the sample relationship would be if the null hypothesis were true.
If the sample relationship would be extremely unlikely, then reject the null hypothesis in favor of the alternative hypothesis. If it would not be extremely unlikely, then retain the null hypothesis .

Following this logic, we can begin to understand why Mehl and his colleagues concluded that there is no difference in talkativeness between women and men in the population. In essence, they asked the following question: “If there were no difference in the population, how likely is it that we would find a small difference of d = 0.06 in our sample?” Their answer to this question was that this sample relationship would be fairly likely if the null hypothesis were true. Therefore, they retained the null hypothesis—concluding that there is no evidence of a sex difference in the population. We can also see why Kanner and his colleagues concluded that there is a correlation between hassles and symptoms in the population. They asked, “If the null hypothesis were true, how likely is it that we would find a strong correlation of +.60 in our sample?” Their answer to this question was that this sample relationship would be fairly unlikely if the null hypothesis were true. Therefore, they rejected the null hypothesis in favor of the alternative hypothesis—concluding that there is a positive correlation between these variables in the population.

A crucial step in null hypothesis testing is finding the likelihood of the sample result if the null hypothesis were true. This probability is called the p value . A low p value means that the sample result would be unlikely if the null hypothesis were true and leads to the rejection of the null hypothesis. A p value that is not low means that the sample result would be likely if the null hypothesis were true and leads to the retention of the null hypothesis. But how low must the p value be before the sample result is considered unlikely enough to reject the null hypothesis? In null hypothesis testing, this criterion is called α (alpha) and is almost always set to .05. If there is a 5% chance or less of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected. When this happens, the result is said to be statistically significant . If there is greater than a 5% chance of a result as extreme as the sample result when the null hypothesis is true, then the null hypothesis is retained. This does not necessarily mean that the researcher accepts the null hypothesis as true—only that there is not currently enough evidence to reject it. Researchers often use the expression “fail to reject the null hypothesis” rather than “retain the null hypothesis,” but they never use the expression “accept the null hypothesis.”

The Misunderstood p Value

“Null Hypothesis” retrieved from http://imgs.xkcd.com/comics/null_hypothesis.png (CC-BY-NC 2.5)

Role of Sample Size and Relationship Strength

Statistical Significance Versus Practical Significance

“Conditional Risk” retrieved from http://imgs.xkcd.com/comics/conditional_risk.png (CC-BY-NC 2.5)

Key Takeaways

Null hypothesis testing is a formal approach to deciding whether a statistical relationship in a sample reflects a real relationship in the population or is just due to chance.
The logic of null hypothesis testing involves assuming that the null hypothesis is true, finding how likely the sample result would be if this assumption were correct, and then making a decision. If the sample result would be unlikely if the null hypothesis were true, then it is rejected in favor of the alternative hypothesis. If it would not be unlikely, then the null hypothesis is retained.
The probability of obtaining the sample result if the null hypothesis were true (the p value) is based on two considerations: relationship strength and sample size. Reasonable judgments about whether a sample relationship is statistically significant can often be made by quickly considering these two factors.
Statistical significance is not the same as relationship strength or importance. Even weak relationships can be statistically significant if the sample size is large enough. It is important to consider relationship strength and the practical significance of a result in addition to its statistical significance.
Discussion: Imagine a study showing that people who eat more broccoli tend to be happier. Explain for someone who knows nothing about statistics why the researchers would conduct a null hypothesis test.
The correlation between two variables is r = −.78 based on a sample size of 137.
The mean score on a psychological characteristic for women is 25 ( SD = 5) and the mean score for men is 24 ( SD = 5). There were 12 women and 10 men in this study.
In a memory experiment, the mean number of items recalled by the 40 participants in Condition A was 0.50 standard deviations greater than the mean number recalled by the 40 participants in Condition B.
In another memory experiment, the mean scores for participants in Condition A and Condition B came out exactly the same!
A student finds a correlation of r = .04 between the number of units the students in his research methods class are taking and the students’ level of stress.
Cohen, J. (1994). The world is round: p < .05. American Psychologist, 49 , 997–1003. ↵
Hyde, J. S. (2007). New directions in the study of gender similarities and differences. Current Directions in Psychological Science, 16 , 259–263. ↵

Share This Book

Increase Font Size

COMMENTS

892: Null Hypothesis
The null hypothesis is the hypothesis in a statistical analysis that indicates that the effect investigated by the analysis does not occur, i.e. 'null' as in zero effect. For example, the null hypothesis for a study about cell phones and cancer risk might be "Cell phones have no effect on cancer risk." The alternative hypothesis, by contrast ...
xkcd: Null Hypothesis
xkcd: Null Hypothesis. A webcomic of romance, sarcasm, math, and language. Special 10th anniversary edition of WHAT IF? —revised and annotated with brand-new illustrations and answers to important questions you never thought to ask—coming from November 2024. Preorder here !
882: Significant
The 5% here is α. The correct interpretation of it is that if the null hypothesis is true, there is a 5% chance that we will mistakenly reject it. P in "P<0.05" is the chance that, if the null hypothesis is true, a result as extreme as, or more extreme than, the result we get from this experiment.
2400: Statistics
The null hypothesis was previously the subject of 892: Null Hypothesis. The null hypothesis is rejected when the probability of something like the observed data would be very low were the null hypothesis true. For a simplified example, imagine there are 10 000 people in the vaccinated group, and each has a 5% chance of catching COVID under the ...
xkcd: Significant
xkcd: Significant. A webcomic of romance, sarcasm, math, and language. Special 10th anniversary edition of WHAT IF? —revised and annotated with brand-new illustrations and answers to important questions you never thought to ask—coming from November 2024. Preorder here ! Significant.
1574: Trouble for Science
The threshold for p-value cutoff, α, is pre-specified (usually 5% or 1%, which is more conservative). When the p-value is lower to or equal to α, the null hypothesis is rejected in favor of the alternative hypothesis. When it is higher than α, the null hypothesis is retained. The value used for α has been proposed by Fisher and is arbitrary.
Null Hypothesis
Retrieved from "https://www.explainxkcd.com/wiki/index.php?title=Null_Hypothesis&oldid=17313"
Null Hypothesis : r/xkcd
All real world experiments aim to disprove a null hypothesis. This is a bit of simplification. The following steps are followed for confirmatory data analysis.. From the given problem statement, independent variables are identified. For simplification of explanation, lets assume we have 2 independent variables for our problem statement.
xkcd: Statistics
We reject the null hypothesis based on the 'hot damn, check out this chart' test. Special 10th anniversary edition of WHAT IF? —revised and annotated with brand-new illustrations and answers to important questions you never thought to ask—coming from November 2024.
2001: Clickbait-Corrected p-Value
I thought this comic was about correcting for any p-hacking that aimed to increase the media presence (and thus the clickbait) of the study.172.68.94.10 17:32, 1 June 2018 (UTC) . The explanation for null hypothesis is correct semantically, it would be accepted if there was no OR negative improvement, however, this is usually stated more succinctly as "will not improve performance" or (in ...
2020: Negative Results
Explain xkcd is a wiki dedicated to explaining the webcomic xkcd. Go figure. 2020: Negative Results. Explain xkcd: It's 'cause you're dumb. ... I don't think the "null hypothesis" is a reference to 892: Null Hypothesis, as the explanation currently says. Sure, the comic doesn't mention any particular null hypothesis, ...
PDF xkcd Significance NoDate
In the comic, the scientists choose a significance level of 5%. If they find that more of the people who ate jelly beans had acne, and the probability that it was a purely random result is less than 1 in 20 (i.e., p < .05), they will reject the null hypothesis (that jelly beans don't cause acne). If however, the probability that their result ...
xkcd: Slope Hypothesis Testing
xkcd: Slope Hypothesis Testing. A webcomic of romance, sarcasm, math, and language. Special 10th anniversary edition of WHAT IF? —revised and annotated with brand-new illustrations and answers to important questions you never thought to ask—coming from November 2024. Preorder here !
xkcd: Null Hypothesis
xkcd: Null Hypothesis. Special 10th anniversary edition of WHAT IF? —revised and annotated with brand-new illustrations and answers to important questions you never thought to ask—coming from November 2024.
xkcd: Frequentists vs. Bayesians
xkcd: Frequentists vs. Bayesians. A webcomic of romance, sarcasm, math, and language. Special 10th anniversary edition of WHAT IF? —revised and annotated with brand-new illustrations and answers to important questions you never thought to ask—coming from November 2024. Preorder here !
PDF xkcd Significance NoDate
This is known as null hypothesis significance testing. Before they start the experiment, they choose a threshold known as the probability level. In the comic, the scientists choose a probability level of 5%. The probability level indicates the probability of obtaining test results at least as extreme as the results actually observed, assuming ...
xkcd: "Curve-fitting methods and the messages they send"
We can't go around linking to xkcd all the time or it would just fill up the blog, but this one is absolutely brilliant. You could use it as the basis for a statistics Ph.D. I came across it in this post from Palko, which is on the topic of that Dow 36,000 guy who keeps falling up and up. But that's another story, related to the idea, which we've discussed many times, that Gresham's ...
xkcd: Hypothesis Generation
xkcd: Hypothesis Generation. A webcomic of romance, sarcasm, math, and language. Special 10th anniversary edition of WHAT IF? —revised and annotated with brand-new illustrations and answers to important questions you never thought to ask—coming from November 2024. Preorder here !
PDF Hypothesis Testing
hypothesisand thealternative hypothesis 2.Choose a sample, collect data, and analyze the data 3.Figure out how likely it is to see data like what we got/observed, IF the null hypothesis were true 4.If our data would have been extremely unlikely if the null claim were true, then we reject it and deem the alternative claim worthy of further study.
Explain the xkcd jelly bean comic: What makes it funny?
I see that one time out of the twenty total tests they run, p < 0.05 p < 0.05, so they wrongly assume that during one of the twenty tests, the result is significant ( 0.05 = 1/20 0.05 = 1 / 20 ). xkcd jelly bean comic - "Significant". Title: Significant. Hover text: "'So, uh, we did the green study again and got no link.
Understanding Null Hypothesis Testing
A crucial step in null hypothesis testing is finding the likelihood of the sample result if the null hypothesis were true. This probability is called the p value. A low p value means that the sample result would be unlikely if the null hypothesis were true and leads to the rejection of the null hypothesis. A high p value means that the sample ...
13.1 Understanding Null Hypothesis Testing
A crucial step in null hypothesis testing is finding the likelihood of the sample result if the null hypothesis were true. This probability is called the p value. A low p value means that the sample result would be unlikely if the null hypothesis were true and leads to the rejection of the null hypothesis. A p value that is not low means that ...
How Does the Null Hypothesis Work?
The null hypothesis is the hypothesis of "no effect," i.e., the hypothesis opposite to the effect we want to test for. In contrast, the alternative hypothesis is the one positing the existence of the effect of interest. 3. Effects and Null Hypothesis. The effect depends on our research question.
Interpretation of rejecting the null hypothesis
5. When the null hypothesis H0: β = 0 H 0: β = 0 is rejected, there are two possible interpretations: (i) the null hypothesis is true but a rare event occurred, or (ii) the null hypothesis is wrong. If β = 0 β = 0 is the truth, then the null hypothesis is correct and the interpretation is only (i) a rare event occurred.

2020: Negative Results

Transcript [ edit ]

Connection to Significant [ edit ]

Navigation menu

Null Hypothesis

Understanding Null Hypothesis Testing

The Purpose of Null Hypothesis Testing

The Logic of Null Hypothesis Testing

Role of Sample Size and Relationship Strength

Statistical Significance Versus Practical Significance

Long Descriptions

Media Attributions

Share This Book

13.1 Understanding Null Hypothesis Testing

The Purpose of Null Hypothesis Testing

The Logic of Null Hypothesis Testing

The Misunderstood p Value

Role of Sample Size and Relationship Strength

Statistical Significance Versus Practical Significance

Key Takeaways

Share This Book

COMMENTS