2001: Clickbait-Corrected p-Value

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Clickbait-Corrected p-Value
When comparing hypotheses with Bayesian methods, the similar 'clickbayes factor' can account for some harder-to-quantify priors.
Title text: When comparing hypotheses with Bayesian methods, the similar 'clickbayes factor' can account for some harder-to-quantify priors.


Ambox notice.png This explanation may be incomplete or incorrect: Please first explain p-value. Most people don't know. - Please change this comment when editing this page. Do NOT delete this tag too soon.
If you can address this issue, please edit the page! Thanks.

This comic references hypothesis testing in statistics. When one wishes to determine if a given assumption is statistically supported by the data, a hypothesis test may be used. Usually, such a test takes the form of a null hypothesis, H0, which basically states that the status quo is maintained (here, that chocolate has no effect on athletic performance). The alternative hypothesis, H1, is the one the statistician wishes to put to the test (here, that chocolate increases athletic performance). Normally, the mathematician would gather data, run some calculations, and come up with a p-value. This is the probability that the sample's results were obtained solely by chance, assuming that the null hypothesis is held to be true. Thus, the lesser the p-value, the less likely the null hypothesis is true. Below a certain point, usually 5% or 1%, we reject H0 and accept H1 to be true.

In this version, the p-value is corrected by a factor which increases when readers click a headline stating that H1 is true, and decreases when people click a headline stating that H0 is true. This has the effect of increasing the p-value if readers favor H1 over H0, leading to a greater chance of H0 being accepted. This seems to operate under the assumption that whatever clickers of clickbait believe, the reverse is likely to be true.

As the statistical results now depend on people's beliefs about the hypothesis, this is as far from actual science as one can get.

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.

This comic calculates the p-value of clickbait article and videos, nowadays very common on the web. The formula depicted is commonly known as the Bayes theorem, and a more common expression of that theorem is p(A|B) = p(B|A) * p(A) / p(B). Here, it depicts the odds of a clickbait article being clicked depending on two different headlines.


Ambox notice.png This transcript is incomplete. Please help editing it! Thanks.

Clickbait-Corrected p-Value :

PCL = Ptraditional * click(H1)/click(H0)}

H0 : NULL hypothesis ("Chocolate has no effect on athletic performance")

H1 : Alternative hypothesis ("Chocolate boosts athletic performance")

click(H) : Fraction of test subjects who click on a headline announcing that H is true

"When comparing hypotheses with Bayesian methods, the similar 'clickbayes factor' can account for some harder-to-quantify priors.

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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. 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 keeping with the language of the comic) "does not boost performance", since that has the same meaning without the unnecessary verbosity. ---- (talk) (please sign your comments with ~~~~)

I can't believe I clicked on this 20:28, 1 June 2018 (UTC)

I've removed a paragraph which claimed that this was an instance of Bayes theorem. Despite some similarity in structure, it is not. Winstonewert (talk) 01:39, 2 June 2018 (UTC)

I was honestly expecting a comic about (or at least referencing) 2001: A Space Odyssey. Herobrine (talk) 07:41, 2 June 2018 (UTC)

If reseachers were to use this adjusted formula, it would make sensational results much harder to demonstrate as significant, and uninteresting results much easier. Seems to me it’s a good adjustment for a lot of things. I wonder about p-values, though ... seems to me a value that is at all borderline just means you don’t have enough data yet for the actual size of the effect you’re measuring, but I don’t know much about statistics. 02:08, 3 June 2018 (UTC)

Ummm. I use a Gecko engine* with "Block Advertisement" checked. *(K-Meleon 76.0) I can see the image from "xkcd Phone 2000" and "LeBron James and Stephen Curry", but NOT THIS PAGE. Unless I uncheck "Block Advertisement". Obviously this is to encourage clicking on things? 09:29, 4 June 2018 (UTC)

This could be an attempt to correct for the effects described in the infamous Iohannides paper:

In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller[...] where there is greater flexibility in designs, [...] where there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true.

-- 23:04, 19 June 2018 (UTC)


This comic is labeled as incomplete, but the explanation seems pretty thorough as it is. Any explanation can be cleaned up ad infinitum to suit people's liking, but this one seems pretty good as it is. Is the incomplete tag still warranted at this point?--Sensorfire (talk) 18:46, 1 October 2018 (UTC)

There were many edits recently because this comic is mentioned at the sitenotice on top here, if you now understand what a p-Value is, feel free to remove that incomplete tag. I personally prefer a more straight forward and shorter explanation. But that's only my opinion. When this comic is not labeled incomplete anymore I will put some else to that sitenotice. --Dgbrt (talk) 21:23, 1 October 2018 (UTC)
If this wiki tracked pageviews, somebody could put forth a hypothesis of something measurable on the site, see how many clicks each hypothesis got, and produce a real clickbait-adjusted p-value for it. 02:52, 5 October 2018 (UTC)
We don't explain clickbait here...--Dgbrt (talk) 19:20, 5 October 2018 (UTC)

Still incomplete because if you google for this "chocolate health" you will understand. --Dgbrt (talk) 19:20, 5 October 2018 (UTC)

true -> so; will -> shall; if and only if -> if; hard -> touh Lysdexia (talk) 07:59, 25 July 2019 (UTC)