To quantify replication outcomes we propose a novel Bayesian replication test that compares the adequacy of two competing hypotheses. The first hypothesis is that of the skeptic and holds that the effect is spurious; this is the null hypothesis that postulates a zero effect size, H0: δ = 0. The second hypothesis is that of the proponent and holds that the effect is consistent with the one found in the original study, an effect that can be quantified by a posterior distribution. Hence, the second hypothesis --the replication hypothesis-- is given by Hr : δ ~ "posterior distribution from original study".Here is the link: Bayesian Tests to Quantify the Result of a Replication Attempt; Josine Verhagen and Eric-Jan Wagenmakers, University of Amsterdam.
Saturday, February 22, 2014
Bayesian Tests to Quantify the Result of a Replication Attempt
This manuscript looks like a nice use of Bayes factors to assess replication results. I have not read it yet in detail, but the idea sounds right on target. From the abstract: