(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
This is the conference, and here’s my talk (will do Google hangout, just as with my recent talks in Bern, Strasbourg, etc):
Hypothesis Testing is a Bad Idea
Through a series of examples, we consider problems with classical hypothesis testing, whether performed using classical p-values or confidence intervals, Bayes factors, or Bayesian inference using noninformative priors. We locate the problem not in the use of any particular statistical method but rather with larger problems of deterministic thinking and a misguided version of Popperianism in which the rejection of a straw-man null hypothesis is taken as confirmation of a preferred alternative. We suggest solutions involving multilevel modeling and informative Bayesian inference.
P.S. More here from 2014.
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