Bayes Factors, the Bayesian tool for hypothesis testing, are receiving increasing attention in the
literature. Compared to their frequentist rivals (p-values or test statistics), Bayes Factors have the
conceptual advantage of providing evidence both for and against a null hypothesis and they can be
calibrated so that they do not depend so heavily on the sample size. However, research on the synthesis
of Bayes Factors arising from individual studies has received very limited attention. In this work we
review and propose methods for combining Bayes Factors from multiple studies, depending on the level
of information available. In the process, we provide insights with respect to the interplay between
frequentist and Bayesian evidence. We also clarify why some intuitive suggestions in the literature can
be misleading. We assess the performance of the methods discussed via a simulation study and apply
the methods in an example from the field of psychology.
Zoom link: https://zoom.us/j/97374751400?pwd=UjZ4cUVaSXZ2bnZUT1BNSVhySFlDQT09
