The growing interest in hair care products has led to an increase in clinical studies assessing their safety and efficacy. As statistical analysis plays a critical role in validating study outcomes, it is essential to evaluate the appropriateness and reporting quality of these methods. This review systematically examines the statistical techniques used in human clinical trials on hair care to assess their suitability, consistency, and transparency. A comprehensive search of the PubMed database identified 22 eligible studies published between January 2020 and April 2025, in accordance with PRISMA guidelines. Key data extracted included study design, sample size, outcome measures, and statistical methods employed. The most frequently used techniques included the Shapiro-Wilk test for normality assessment; paired and independent t-tests; Wilcoxon signed-rank and Mann-Whitney U tests for group comparisons; and Chi-square or Fisher’s exact tests for categorical data. More complex studies incorporated repeated measures ANOVA, ANCOVA, or linear mixed-effects models to manage longitudinal or multivariable data. While the majority of studies applied statistically appropriate methods, significant variation was observed in the reporting of test assumptions, effect sizes, and justification for statistical choices. Such inconsistencies may limit the reproducibility and interpretability of findings. This review highlights the need for improved standardization and transparency in the application and reporting of statistical methods in hair care research to strengthen the quality, reliability, and comparability of future clinical evidence.
Patel et al. (Thu,) studied this question.