> If clinical trialists use p-values wrong, how is moving to Bayesian methods going to be less misused and misunderstood? Bayesian methods have the advantage t

> If clinical trialists use p-values wrong, how is moving to Bayesian methods going to be less misused and misunderstood? Bayesian methods have the advantage that, while they can still be misused and misunderstood, at least when used correctly they tell us what we want to know and are easy to interpret (a posterior probability is exactly what you think it is) whereas p-values are hard to interpret correctly (a low p-value can imply a large effect size, a large sample size, or in the face of publication bias, lord knows what). That said, I do think that the advantage to Bayesian thinking in research would mostly not be about the methods but about the attitude: aside from a couple of weirdos pushing Bayes factors, Bayesian statisticians almost universally communicate results using credible intervals (HPD) instead of dichotomizing the evidence into significant or not significant. Frequentist confidence intervals will get you most of the way there and could completely replace p-values, but if you're going to advocate for better statistics and uproot established practices, might as well go all the way and encourage better methods and better ways of communicating results at the same time.