Improving Statistical Reporting in Psychology

Transparent and comprehensive statistical reporting is critical for ensuring the credibility, reproducibility, and interpretability of psychological research. This paper offers a structured set of guidelines for reporting statistical analyses in empirical psychology, emphasizing clarity at both the planning and results stages. Drawing on established recommendations and emerging best practices, we outline key decisions related to hypothesis formulation, sample size justification, preregistration, outlier and missing data handling, statistical model specification, and the interpretation of inferential outcomes. We address considerations across frequentist, Bayesian, and sequential frameworks, including guidance on effect size reporting, equivalence testing, and the appropriate treatment of null results. To bridge the gap between theory and practice, we provide a curated list of freely available tools, packages, and functions that researchers can use to implement transparent reporting practices in their own analyses. To illustrate the practical application of these principles, we provide a side-by-side comparison of poor versus best-practice reporting using a hypothetical cognitive psychology study. By adopting transparent reporting standards, researchers can improve the robustness of individual studies and facilitate cumulative scientific progress through more reliable meta-analyses and research syntheses.