Arguments that machine learning (ML) is facing a reproducibility and replication crisis
suggest that some published claims in research cannot be taken at face value. Concerns …

It is now widely accepted that the standard inferential toolkit used by the scientific research
community—null‐hypothesis significance testing (NHST)—is not fit for purpose. Yet despite …

Replicability Across Multiple Studies Page 1 Statistical Science 2023, Vol. 38, No. 4, 602–620
https://doi.org/10.1214/23-STS892 © Institute of Mathematical Statistics, 2023 Replicability …

A perennial objection against Bayes factor point-null hypothesis tests is that the point-null
hypothesis is known to be false from the outset. We examine the consequences of …

The ongoing replication crisis in science has increased interest in the methodology of
replication studies. We propose a novel Bayesian analysis approach using power priors …

The influential claim that most published results are false raised concerns about the
trustworthiness and integrity of science. Since then, there have been numerous attempts to …

Replication studies are essential for assessing the credibility of claims from original studies.
A critical aspect of designing replication studies is determining their sample size; a too-small …

Predictive modeling uncovers knowledge and insights regarding a hypothesized data
generating mechanism (DGM). Results from different studies on a complex DGM, derived …

We study a statistical framework for replicability based on a recently proposed quantitative
measure of replication success, the sceptical pp‐value. A recalibration is proposed to obtain …

Determining an appropriate sample size is a critical element of study design, and the
method used to determine it should be consistent with the planned analysis. When the …