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|| 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. Low p-values occur when the results appear to reject the null hypothesis, whereas the high p-values suggest no relation between the hypothesis and the real world.
The p-value calculated from the experiment data is used to interpret whether the experiment was significant and supports the hypothesis.
The significance threshold (usually 0.05) should be set prior the experiment in order to avoid 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 sneezes. 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.
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. Notice that there is another mistake in the title text, specifically the word "significant" is misspelled as "signifcant". This is quite possibly not an author's error and rather another comical pun that further supports the "hope no one notices" theme featured in the same sentence.
|| This explanation may be incomplete or incorrect: First draft.|
There are two columns in a T-table labelled "p-value" and "interpretation". The interpretation column selects various areas of the P-value column.
|| Highly significant
|| Oh crap. Redo calculations.
|| On the edge of significance
|| Highly suggestive, relevant at the p<0.10 level
|| Hey, look at this interesting subgroup analysis
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