The Prevalence of Misreporting and Misinterpreting Correlation Coefficients in Biomedical Literature

TL;DR

This study systematically reviews the misuse and misinterpretation of correlation coefficients in biomedical literature, revealing widespread poor reporting and advocating for better statistical standards to improve research transparency and reproducibility.

Contribution

It provides a comprehensive analysis of correlation reporting practices in top journals and proposes guidelines to improve statistical clarity in biomedical research.

Findings

58.71% studies did not report sample sizes

98.06% failed to provide confidence intervals

Over 45% relied solely on point estimates for correlation

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.