Editing 2001: Clickbait-Corrected p-Value
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==Explanation== | ==Explanation== | ||
− | {{ | + | {{incomplete|Click here to learn more about the influence of Clickbait... But please first explain p-value. Most people don't know. And more wiki links.}} |
+ | This is yet another comic dealing with [[:Category:Clickbait|Clickbait]], and is satire mocking researchers/journalists/publishers for fudging research data based on what brings in the most advertising revenue. The topic of fudging research data in academia has also previously appeared in [[882: Significant]] and [[1478: P-Values]]. | ||
− | + | Clickbait is the practice of using deceptive or manipulative headlines to entice readers to click on a dubious news story, often with the purpose of generating ad revenue. | |
− | + | {{w|Statistical hypothesis testing|Hypothesis testing}} in statistics is a standard method to determine whether a particular hypothesis is supported by the data. For the topic given in this comic, a researcher might compare data on athletic performance with data on chocolate consumption by those athletes to determine whether the two trend together. By convention, the "null hypothesis" (designated H<sub>0</sub>) is that there's no correlation (that chocolate isn't correlated with athletic performance, in this case) and the "alternate hypothesis" (H<sub>1</sub>) is that they are correlated. (If the study consists of ''feeding'' chocolate to one of two identical groups and not the other, rather than tracking what they'd be eating anyway, then the alternative hypothesis can be strengthened to be that chocolate *causes* improved performance.) These sets are subjected to statistical tests which return a "test statistic". From that test statistic a {{w|P-value|"p-value"}} is calculated. The p-value indicates the probability of observing the obtained results (or any more extreme value), when all assumptions of the test are true (including the null hypothesis). | |
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+ | In layman's terms: The p-value is the probability that the researcher sees results as extreme or more extreme than the observed result given the null hypothesis is true; [http://www.perfendo.org/docs/BayesProbability/twelvePvaluemisconceptions.pdf the p-value is NOT the probability that the null hypothesis is correct]. It answers the question: If there is no correlation, how likely was it that I saw a correlation at least this big? Hence, if the p-value is low enough (by convention < 0.05), the null hypothesis is rejected, and we conclude that the alternate hypothesis is supported by the data (NOT that it is "correct" or "true"). | ||
In this comic, the p-value is corrected by a factor that takes clickbait into account. This factor has the effect of increasing the p-value if H<sub>1</sub> is more clickbaity than H<sub>0</sub>, and decreases the p-value if H<sub>0</sub> is more clickbaity than H<sub>1</sub>. This suggests that whatever clickers of clickbait believe, the reverse is likely to be true. | In this comic, the p-value is corrected by a factor that takes clickbait into account. This factor has the effect of increasing the p-value if H<sub>1</sub> is more clickbaity than H<sub>0</sub>, and decreases the p-value if H<sub>0</sub> is more clickbaity than H<sub>1</sub>. This suggests that whatever clickers of clickbait believe, the reverse is likely to be true. | ||
− | + | Or, another interpretation could be that this factor corrects for a selection bias effect where the p-values for more clickbaity H<sub>1</sub>s tend to be lower than they should be and p-values for non-clickbaity H<sub>0</sub>s to be higher than they should be. For example, one explanation could be that for p-values that are on the cusp of significance, researchers may be more incentivized to fudge and adjust the data to get the p-value down if the H<sub>1</sub> is highly sensational, since the H<sub>1</sub> would make the research more likely to get published and attract attention. (See also [https://fivethirtyeight.com/features/science-isnt-broken/ FiveThirtyEight's article on p-hacking] and [https://stats.stackexchange.com/questions/200745/how-much-do-we-know-about-p-hacking-in-the-wild/200752#200752 this Stack Exchange question about p-hacking in the wild].) | |
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− | + | As the statistical results now depend on people's beliefs about the hypothesis, this is as far from actual science as one can get. However, in a way, it is more in tune with a quote by Arbuthnot (one of the originators of the use of p-values) attributing variation to active thought rather than chance, "From whence it follows, that it is Art, not Chance, that governs." Randall applying that quote to the thoughts of the masses, bringing it in line with "Art". | |
[[1475: Technically|Technically]], the comic's depiction of null and alternative hypotheses is not entirely correct. As the alternative hypothesis (H<sub>1</sub>) predicts that chocolate will ''improve performance'' (i.e., a one-tailed, directional hypothesis), the null hypothesis (H<sub>0</sub>) should predict that chocolate will do nothing ''or'' make performance worse. In other words, the alternative hypothesis should be true if and only if the null hypothesis is false. For example, alternatively, if the H<sub>1</sub> were to say that ''chocolate will change performance'' (for better or worse; i.e., a two-tailed hypothesis) then H<sub>0</sub> should say that ''chocolate will do nothing''. | [[1475: Technically|Technically]], the comic's depiction of null and alternative hypotheses is not entirely correct. As the alternative hypothesis (H<sub>1</sub>) predicts that chocolate will ''improve performance'' (i.e., a one-tailed, directional hypothesis), the null hypothesis (H<sub>0</sub>) should predict that chocolate will do nothing ''or'' make performance worse. In other words, the alternative hypothesis should be true if and only if the null hypothesis is false. For example, alternatively, if the H<sub>1</sub> were to say that ''chocolate will change performance'' (for better or worse; i.e., a two-tailed hypothesis) then H<sub>0</sub> should say that ''chocolate will do nothing''. | ||
− | + | For the title text: Bayesian methods start with a "prior", which is the probabilities believed before seeing new evidence (e.g. before conducting an experiment). Time spent reading clickbait would probably cause people to have unusual beliefs about what is likely before seeing evidence. | |
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==Transcript== | ==Transcript== | ||
− | :[Under a heading that says Clickbait-Corrected p-Value there is a | + | :[Under a heading that says Clickbait-Corrected p-Value there is a mathematic formula. Below that is the description of the two used variables and what they mean:] |
:Clickbait-corrected p-value: | :Clickbait-corrected p-value: | ||
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[[Category:Clickbait]] | [[Category:Clickbait]] | ||
[[Category:Statistics]] | [[Category:Statistics]] | ||
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