Abstract Bayesian hypothesis testing via Bayes factors offers a principled alternative to classical p -value methods in meta-analysis, particularly suited to its cumulative and sequential nature. Unlike p -values, Bayes factors allow for quantifying support both for and against the existence of an effect, facilitate ongoing evidence monitoring, and maintain coherent long-run behavior as additional studies are incorporated. Recent theoretical developments further show how Bayes factors can flexibly control Type I error rates through connections to e-value theory. Despite these advantages, their use remains limited in the meta-analytic literature. This article provides a critical overview of their theoretical properties, methodological considerations—such as prior sensitivity—and practical advantages for evidence synthesis. Two illustrative applications are provided: one on statistical learning in individuals with language impairments, and another on seroma incidence following post-operative exercise in breast cancer patients. New tools supporting these methods are available in the open-source R package BFpack.
Mulder et al. (Thu,) studied this question.