There is a growing interest in the analysis of replication studies of original findings across many disciplines. When testing a hypothesis for an effect size, two Bayesian approaches stand out for their principled use of the Bayes factor (BF), namely the replication BF and the skeptical BF. In particular, the latter BF is based on the skeptical prior, which represents the opinion of an investigator who is unconvinced by the original findings and wants to challenge them. We embrace the skeptical perspective, and elaborate a novel mixture prior which incorporates skepticism while at the same time controlling for prior-data conflict within the original data. Consistency properties of the resulting skeptical mixture BF are provided together with an extensive analysis of the main features of our proposal. Finally, we apply our methodology to data from the Social Sciences Replication Project. In particular we show that, for some case studies where prior-data conflict is an issue, our method uses a more realistic prior and leads to evidence-classification for replication success which differs from the standard skeptical approach.

翻译：跨学科对原始发现的复制研究分析日益受到关注。在检验效应量假设时，两种贝叶斯方法因合理使用贝叶斯因子（BF）而脱颖而出，即复制BF和怀疑BF。其中，后者基于怀疑先验，代表了对原始发现持怀疑态度并希望挑战这些发现的研究者观点。我们采纳怀疑视角，并构建了一种新颖的混合先验，该先验在融入怀疑态度的同时，还控制了原始数据中的先验数据冲突。我们提供了由此产生的怀疑混合BF的一致性性质，并对其主要特征进行了广泛分析。最后，我们将该方法应用于社会科学复制项目的数据。特别地，我们展示出，在某些先验数据冲突成为问题的案例研究中，我们的方法采用了更现实的先验，并得出了与标准怀疑方法不同的复制成功证据分类结果。