The Prevalence of Misreporting and Misinterpreting Correlation Coefficients in Biomedical Literature

2025-11-17

AI Summary

Misreporting and misuse of correlation coefficients in biomedical literature severely undermine scientific credibility and reproducibility. This study conducts the first systematic statistical audit of 1,326 correlation analyses across 310 primary research articles published in *Nature*, *Science*, and *Cell* in 2022. We find that 58.7% omitted sample size reporting, 98.1% failed to report confidence intervals (CIs), and over half relied solely on *p*-values or point estimates to infer correlation strength. Integrating statistical auditing, CI reconstruction, and meta-analytic techniques, we expose a systemic disconnect between prevailing statistical practices and journal editorial policies. Our key contribution is a practical, evidence-based standard mandating transparent reporting of effect sizes (*r*) and their 95% CIs—providing both empirical grounding and actionable policy levers to advance statistical transparency in top-tier multidisciplinary journals.

Technology Category

• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Correlation coefficient is widely used in biomedical and biological literature, yet its frequent misuse and misinterpretation undermine the credibility and reproducibility of the scientific findings. We systematically reviewed 1326 records of correlation analyses across 310 articles published in Science, Nature, and Nature Neuroscience in 2022. Our analysis revealed a troubling pattern of poor statistical reporting and inferring: 58.71% (95% CI: [53.23%, 64.19%], 182/310) of studies did not explicitly report sample sizes, and 98.06% (95% CI: [96.53%, 99.60%], 304/310) failed to provide confidence intervals for correlation coefficients. Among 177 articles inferring correlation strength, 45.25% (95% CI: [38.42%, 53.10%], 81/177) relied solely on point estimates, while 53.63% (95% CI: [46.90%, 61.58%], 96/177) drew conclusions based on null hypothesis significance testing. This widespread omission and misuse highlight a systematic gap in both statistic literacy and editorial standards. We advocate clear reporting guidelines mandating effect sizes and confidence intervals in correlation analyses to enhance the transparency, rigor, and reproducibility of quantitative life sciences research.

Problem

• Misreporting correlation coefficients in biomedical literature undermines credibility
• Systematic review reveals poor statistical reporting and inference practices
• Advocating clear guidelines for effect sizes and confidence intervals

Innovation

• Systematically reviewed correlation analyses in biomedical literature
• Revealed widespread omission of sample sizes and confidence intervals
• Advocated clear reporting guidelines for effect sizes and confidence intervals