Here are my slides from the ASA Symposium on Statistical Inference : “A World Beyond p < .05” in the session, “What are the best uses for P-values?”. (Aside from me,our session included Yoav Benjamini and David Robinson, with chair: Nalini Ravishanker.)
- Why use a tool that infers from a single (arbitrary) P-value that pertains to a statistical hypothesis H0 to a research claim H*?
- Why use an incompatible hybrid (of Fisher and N-P)?
- Why apply a method that uses error probabilities, the sampling distribution, researcher “intentions” and violates the likelihood principle (LP)? You should condition on the data.
- Why use methods that overstate evidence against a null hypothesis?
- Why do you use a method that presupposes the underlying statistical model?
- Why use a measure that doesn’t report effect sizes?
- Why do you use a method that doesn’t provide posterior probabilities (in hypotheses)?