Over the last few months, there has been a lot of frenzy in a small portion of the blogosphere over the “ban of P-values” by the Psych magazine Basic and Applied Social Psychology (BASP). You can read the full editorial here.
My goal is to create a series of posts covering the items discussed in the editorial. This will include talking about the evolution of hypothesis testing, p-values and confidence intervals.
Some of the highlights from the editorial are below.
1) p < .05 is too easy and leads to low quality papers:
we believe that the p < .05 bar is too easy to pass and sometimes serves as an excuse for lower quality research.
There has been a lot of papers about the traditional approach of using p < .05 or even <.01 as being arbitrary values. I welcome the reader to check out this webpage by Chris Fraley, which has a collection of articles and papers about Null Hypothesis Significance Testing (NHST) and p-values.
2) Confidence Intervals are no better either:
Analogous to how the NHSTP fails to provide the probability of the null hypothesis, which is needed to provide a strong case for rejecting it, confidence intervals do not provide a strong case for concluding that the population parameter of interest is likely to be within the stated interval.
To me, this is very interesting. I have always relied on confidence intervals to get a bound on the uncertainty around my statistic. The magazine banned the use of Confidence Intervals as well.
Interestingly enough, Bayesian procedures are not “banned”.
Bayesian procedures are neither required nor banned from BASP.