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remain. '''[[Help:How to add a new comic explanation|Add yours]]''' while there's a chance!
 
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Revision as of 11:28, 21 March 2013

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We have collaboratively explained Expression error: Unrecognised punctuation character ",". xkcd comics, and only Expression error: Unrecognised punctuation character ",". (Expression error: Unrecognised punctuation character ",".%) remain. Add yours while there's a chance!

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Go to this comic explanation

P-Values
If all else fails, use "significant at a p>0.05 level" and hope no one notices.
Title text: If all else fails, use "significant at a p>0.05 level" and hope no one notices.

Explanation

Ambox notice.png This explanation may be incomplete or incorrect: Needs work to improve readability for non-statisticians.

This comic plays on how the significance of scientific experiments is measured and interpreted. The p-value is a statistical measure of how well the results of an experiment fit with the results predicted by the hypothesis. In lay terms, p is the probability that random chance can explain the results, without the experimental prediction. When low p-values occur, the results appear to reject the null hypothesis, whereas high p-values suggest no relation between the hypothesis and the real world.

Appropriate experimental design generally requires that the significance threshold (usually 0.05) be set prior the experiment, not allowing ex-post changes in order to get a better experiment report. A simple change of this threshold (e.g. from 0.05 to 0.1) can change the experiment result with p-value=0.06 from "barely significant" to "significant".

The highest p-value at which most studies typically draw significance is p<0.05, which is why all p-values in the comic below that number are marked at least significant. 0.050 is labeled "Oh crap. Redo calculations," because the p-value is very close to being considered significant, but isn't. Redoing the calculations may result in a different answer, but it is not guaranteed that it will be lower than 0.050. Values that are higher than 0.050 and lower than .1 are considered to be suggesting significance without actually supporting it, which will likely support additional trials.

Values higher than .1 should be considered not significant at all, however the comic suggests taking a part of the sample (a "subgroup") and analyzing that subgroup without regard to the rest of the sample. For example, in a study trying to prove that people always sneeze when walking by a particular street lamp, someone would record the number of people who pass the lamp and the number of people who sneeze. If the results don't get the desired p<0.1, then pick a subgroup (e.g. OK, not all people sneeze, but look! women sneeze more than men, so let's analyze only women). Of course, this is not accepted scientific procedure as it's very likely to add sampling bias to the result. This is an example of the Multiple comparisons problem.

The title text suggests that, if the results cannot be normally considered significant, to invert p<0.050, making it p>0.050. This is intended to fool casual readers, as the change is only to the inequality sign, which may go unnoticed.

Transcript

[A two columns T-table where the interpretation column selects various areas of the first column using square brackets.]
P-value Interpretation
0.001 Highly significant
0.01
0.02
0.03
0.04 Significant
0.049
0.050 Oh crap. Redo calculations.
0.051 On the edge of significance
0.06
0.07 Highly suggestive, relevant at the p<0.10 level
0.08
0.09
0.099
≥0.1 Hey, look at this interesting subgroup analysis

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