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There is a growing interest in the analysis of replication studies oforiginal findings across many disciplines. When testing a hypothesis for aneffect size, two Bayesian approaches stand out for their principled use of theBayes factor (BF), namely the replication BF and the skeptical BF. Inparticular, the latter BF is based on the skeptical prior, which represents theopinion of an investigator who is unconvinced by the original findings andwants to challenge them. We embrace the skeptical perspective, and elaborate anovel mixture prior which incorporates skepticism while at the same timecontrolling for prior-data conflict within the original data. Consistencyproperties of the resulting skeptical mixture BF are provided together with anextensive analysis of the main features of our proposal. Finally, we apply ourmethodology to data from the Social Sciences Replication Project. In particularwe show that, for some case studies where prior-data conflict is an issue, ourmethod uses a more realistic prior and leads to evidence-classification forreplication success which differs from the standard skeptical approach.

Assessing replication success via skeptical mixture priors