The sceptical Bayes factor for the assessment of replication success

TL;DR

This paper introduces a new Bayesian criterion called the sceptical Bayes factor for assessing replication success, integrating reverse-Bayes analysis and hypothesis testing to improve replicability evaluation.

Contribution

It proposes a novel Bayesian approach that combines reverse-Bayes analysis with hypothesis testing to provide a more comprehensive measure of replication success.

Findings

The sceptical Bayes factor effectively combines evidence against the null with consistency between studies.

It ensures both studies show sufficient evidence against the null hypothesis.

The method demonstrates advantages in case studies from social sciences.

Abstract

Replication studies are increasingly conducted but there is no established statistical criterion for replication success. We propose a novel approach combining reverse-Bayes analysis with Bayesian hypothesis testing: a sceptical prior is determined for the effect size such that the original finding is no longer convincing in terms of a Bayes factor. This prior is then contrasted to an advocacy prior (the reference posterior of the effect size based on the original study), and replication success is declared if the replication data favour the advocacy over the sceptical prior at a higher level than the original data favoured the sceptical prior over the null hypothesis. The sceptical Bayes factor is the highest level where replication success can be declared. A comparison to existing methods reveals that the sceptical Bayes factor combines several notions of replicability: it ensures that both studies show sufficient evidence against the null and penalises incompatibility of their effect estimates. Analysis of asymptotic properties and error rates, as well as case studies from the Social Sciences Replication Project show the advantages of the method for the assessment of replicability.