Statistical Methods for Replicability Assessment

• Activity Number: 227 - The Best of Annals of Applied Statistics
• Type: Invited
• Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
• Sponsor: IMS
• Author(s): Kenneth Hung* and William Fithian
• Companies: Core Data Science, Facebook and University of California, Berkeley
• Keywords: replicability; multiple testing; postselection inference; publication bias; meta-analysis

Abstract

Large-scale replication studies like the Reproducibility Project: Psychology (RP:P) provide invaluable systematic data on scientific replicability, but most analyses and interpretations of the data fail to agree on the definition of “replicability” and disentangle the inexorable consequences of known selection bias from competing explanations. We discuss three concrete definitions of replicability based on: (1) whether published findings about the signs of effects are mostly correct, (2) how effective replication studies are in reproducing whatever true effect size was present in the original experiment and (3) whether true effect sizes tend to diminish in replication. We apply techniques from multiple testing and postselection inference to develop new methods that answer these questions while explicitly accounting for selection bias. Our analyses suggest that the RP:P dataset is largely consistent with publication bias due to selection of significant effects. The methods in this paper make no distributional assumptions about the true effect sizes.