  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.
