# Statistical Methods for Replicability Assessment

## TL;DR

This paper introduces new statistical methods to assess replicability in scientific studies, addressing issues like selection bias and different definitions of what constitutes a successful replication.

## Contribution

It proposes novel techniques from multiple testing and post-selection inference that explicitly account for selection bias without relying on distributional assumptions.

## Key findings

- RP:P data is consistent with publication bias due to selection of significant effects
- New methods clarify the impact of selection bias on replicability assessments
- Different definitions of replicability lead to varied interpretations of the data

## 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 post-selection 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.