Assessment of replicability is critical to ensure the quality and rigor of scientific research. In this paper, we discuss inference and modeling principles for replicability assessment. Targeting distinct application scenarios, we propose two types of Bayesian model criticism approaches to identify potentially irreproducible results in scientific experiments. They are motivated by established Bayesian prior and posterior predictive model-checking procedures and generalize many existing replicability assessment methods. Finally, we discuss the statistical properties of the proposed replicability assessment approaches and illustrate their usages by simulations and examples of real data analysis, including the data from the Reproducibility Project: Psychology and a systematic review of impacts of pre-existing cardiovascular disease on COVID-19 outcomes.

翻译：评估可复制性对于确保科学研究的质量和严格性至关重要。在本文件中,我们讨论了可复制性评估的推论和示范原则。针对不同的应用设想,我们提出了两种贝叶斯模式批评方法,以确定科学实验中可能无法复制的结果。这些批评方法的动机是既定的先前和后继巴伊西亚预测性模式检查程序,并概括了许多现有的可复制性评估方法。最后,我们讨论了拟议可复制性评估方法的统计特性,并通过模拟和真实数据分析实例,包括“可复制性项目:心理学和系统审查先前存在的心血管疾病对COVID-19结果的影响”的数据,来说明其使用情况。