3 ms·
There are so many problems with this: 1) The p-value filter leads to publication bias. -You should publish your results anyway, or the study wasn't designed/p
by nonbel 9y ago
There are so many problems with this:
1) The p-value filter leads to publication bias.
-You should publish your results anyway, or the study wasn't designed/performed correctly. The raw data and description of methods should be valuable.
2) The null hypothesis is (almost) always false anyway.
-Everything in bio/psych/etc has a real (not spurious) non-zero correlation with everything else, so the significance level just determines how much data needs to be collected to reject it.
3) Rejection or not of the null hypothesis does not indicate whether the theory/explanation of interest is correct, so is inappropriate for deciding whether a result is interesting to begin with.
-Usually the null hypothesis is very precise and the "alternative statistical hypothesis" that maps to the research hypothesis is very vague, so many alternative research hypotheses may explain the results.
- marcosdumay 9y agoI would add that 4) Studies with a large p-value but that are not contradicted by others are much more valuable than studies that have a small p-value but all contradict each other.
- nonbel 9y agoYes, these p-values are irrelevant in every way. What matters is that people figured out a way to get reproducible results. If I can be confident that an "effect" is smaller than x, that is good information. A bunch of studies with slightly different methods with small p-values in opposite directions don't give any confidence at all.