Statistical Perspectives on Reproducibility: Definitions and Challenges

Abstract Reproducibility is a widely discussed topic, yet many experimental results cannot be confirmed due to factors such as publication bias, poor documentation, and inappropriate statistical methods. A lack of standard definitions for reproducibility and related terms further complicates the matter. This paper reviews the literature on reproducibility, clarifies key terminology by defining five types of reproducibility, and addresses variations in published definitions by considering changes in datasets, labs, and experimental conditions. We explore the causes of low reproducibility in scientific studies and discuss statistical perspectives on quantifying and improving reproducibility. In particular, we propose framing statistical reproducibility as a predictive problem, providing a framework to evaluate and address reproducibility challenges.