What does it mean to measure the replicability of scientific results? Is it merely determining whether results are consistent across studies? Much of the existing literature on replicability draws conclusions exclusively from the comparison of study results, in terms of observed estimates, uses a variety of different metrics, and ultimately collapses the comparison into a binary verdict of success or failure. This fails to differentiate between repeated errors and corroboration of the shared generative effect. We develop a unified Bayesian framework in which distinct but related notions of replicability are formally defined and jointly embedded within a single inferential structure. A single model answers four complementary questions: agreement among independent studies; consistency between the individual studies and the shared generative effect; agreement between an existing body of evidence and a new study from the same super-population; inference on the shared generative effect. In practice, we suggest quantifying replicability through posterior probabilities relative to regions of practical relevance. We illustrate these ideas in an eQTL (expression quantitative trait locus) analysis assessing the replicability of a regulatory effect across human ancestral groups.

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