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.
翻译:贝叶斯系数是贝叶斯的假设测试工具,在文献中日益受到重视。比起其常客竞争对手(美元价值或测试统计数字),贝叶斯系数具有提供证据支持和反对无效假设的概念优势,可以加以校准,使其不严重依赖抽样规模。然而,对个别研究产生的贝叶斯系数综合研究的关注非常有限。在这项工作中,我们根据现有信息水平审查并提出了将贝叶斯系数与多项研究相结合的方法。在这个过程中,我们提供了关于常客和贝叶斯证据相互作用的见解。我们还澄清文献中的一些直觉建议为什么会误导我们,我们通过模拟研究来评估讨论的方法的绩效,并在心理学领域举例应用这些方法。