The absence of statistician involvement and low journal impact factor predict statistical mistakes in dermatology journal articles: A cross-sectional analysis

Background: Statistical mistakes can undermine research credibility. Identifying common errors may help researchers avoid them in future studies.
Objective: This study evaluated the frequency and types of statistical mistakes in dermatology journal articles and identified article characteristics that predict these errors.
Methods: A cross-sectional analysis was conducted on articles published in the 2023 volumes of eight dermatology journals. Articles were screened for statistical tests, with a target sample of 200 selected pseudorandomly. Multivariable logistic regressions assessed predictors of statistical mistakes, including journal impact factor, statistician involvement, funding source, first author highest degree, and statistical package.
Results: Of the 189 articles analyzed, 78% contained at least one statistical mistake. Reporting mistakes were found in 67%, and test selection errors in 46%. The absence of statistician involvement (aOR 2.49, p=.03) and low journal impact factor (aOR 3.82, p=.02) predicted the presence of at least one mistake.
Limitations: This sample from eight journals is not representative of all dermatology literature. Original data was not available for testing of test assumptions, so appropriate test selection was determined using statistical conventions.
Conclusion: Statistical mistakes are prevalent in dermatology literature. Researchers should review statistical best practices and consider involving a statistician in their work.