IMHO the current explanation is misleading. The p-value describes how well the experiment output fits hypothesis. The hypothesis can be that the experiment output is random. The low p-values point out that the experiment output fits well with behavior predicted by the hypothesis. The higher the p-value the more the observed and predicted values differ.Jkotek (talk) 08:54, 26 January 2015 (UTC)
I read this comic as a bit of a jab at either scientists or media commentators who want the experiments to show a particular result. As the significance decreases, first they re-do the calculations either in the hope that result might have been erroneous and would be re-classified as significant, or intentionally fudge the numbers to increase the significance. The next step is to start clutching at straws, admitting that while the result isn't Technically significant, it is very close to being significant. After that, changing the language to 'suggestive' may convince the general public that the result is actually more significant than it is, while also changing the parameters of the 'significance' value allows it to be classified as significant. Finally, they give up on the overall results, and start pointing out small sections which may by chance show some interesting features.
All of these subversive efforts could come about because of scientists who want their experiment to match their hypothesis, journalists who need a story, researchers who have to justify further funding etc etc. --Pudder (talk) 09:01, 26 January 2015 (UTC)
- I like how you have two separate categories - "scientists" and "researchers" with each having two different goals :) Nyq (talk) 10:12, 26 January 2015 (UTC)
- As a reporter, I can assure you that journalists are not redoing calculations on studies. Journalists are notorious for their innumeracy; the average reporter can barely figure the tip on her dinner check. Most of us don't know p-values from pea soup.126.96.36.199 16:44, 26 January 2015 (UTC)