# Improving statistical reporting in psychology

## TL;DR

This paper provides practical guidelines and tools to improve statistical reporting in psychology for better credibility and reproducibility.

## Contribution

A structured checklist and resources for transparent statistical reporting in psychology are introduced.

## Key findings

- A TSRP Checklist is provided to evaluate and improve statistical reporting practices.
- Guidance is given for reporting across frequentist, Bayesian, and sequential research designs.
- Tools and examples are offered to bridge theory and practice in statistical reporting.

## Abstract

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 quantitative 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 and Bayesian frameworks and fixed as well as sequential research designs, including guidance on effect size reporting, equivalence testing, and the appropriate treatment of null results. To facilitate implementation of these recommendations, we provide the Transparent Statistical Reporting in Psychology (TSRP) Checklist that researchers can use to systematically evaluate and improve their statistical reporting practices (https://osf.io/t2zpq/). In addition, 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 bridge the gap between theory and practice. To illustrate the practical application of these principles, we provide a side-by-side comparison of insufficient 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.

Practical guidelines for transparent statistical reporting in quantitative psychology are presented, covering key decisions from study planning through results reporting across frequentist, Bayesian, and sequential frameworks. Resources include the Transparent Statistical Reporting in Psychology (TSRP) Checklist as well as specific R packages, functions, and side-by-side comparisons of insufficient versus best-practice reporting.