LQPB2503 - CMLE - Parametric Versus Nonparametric Tests in Repeated Measures Analysis: A Case Study on Hemolysis in Phlebotomy-Parametric Versus Nonparametric Tests in Repeated Measures Analysis: A Case Study on Hemolysis in Phlebotomy

Parametric Versus Nonparametric Tests in Repeated Measures Analysis: A Case Study on Hemolysis in Phlebotomy

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This document explores the statistical analysis of repeated measures data in a clinical laboratory setting investigating hemolysis variation during blood collection at different times of day. The study involved 30 adult outpatients donating blood in morning, midday, and evening sessions, measuring free hemoglobin concentration as a hemolysis marker.<br /><br />Initially, a repeated measures ANOVA (RM-ANOVA) was performed, revealing significant differences, specifically lower midday hemolysis compared to morning and evening. However, the phlebotomist failed to verify key assumptions essential to RM-ANOVA validity, such as sphericity (equal variance of differences), normality, and absence of outliers. Mauchly’s test indicated a violation of sphericity, and outliers were present, compromising parametric analysis reliability. The application of Greenhouse–Geisser correction was suggested but not sufficient given the data issues.<br /><br />A more robust nonparametric alternative, the Friedman test, was then applied. This rank-based method, which does not assume normality or sphericity, confirmed significant differences and uniquely detected a significant difference between morning and evening hemolysis levels that RM-ANOVA missed. Further analysis categorized data ordinally, reaffirming these findings.<br /><br />The document emphasizes the importance of verifying statistical assumptions before analysis, especially in small-sample repeated measures typical in laboratory research. While RM-ANOVA offers more power when assumptions hold true, nonparametric approaches like the Friedman test provide greater robustness against violations such as skewness and outliers. Recent methodological advances allow exact p-value computation for Friedman tests, enhancing accuracy in small samples.<br /><br />For laboratory professionals, these insights highlight the need for critical engagement with statistical methods to ensure valid conclusions that inform clinical practice. Appropriate method selection enhances result reliability, supports evidence-based improvements in venipuncture protocols, and improves patient care outcomes. The study exemplifies how rigorous statistical evaluation strengthens scientific credibility in laboratory diagnostics.

Keywords

repeated measures ANOVA

hemolysis variation

blood collection timing

nonparametric Friedman test

statistical assumptions

sphericity violation

outlier impact

Greenhouse–Geisser correction

clinical laboratory statistics

venipuncture protocol evaluation