Experimental Comparison of Parametric Versus Nonparametric Analyses of Data From the Cold Pressor Test

Abstract

Parametric statistical methods are common in human pain research. They require normally distributed data, but this assumption is rarely tested. The current study analyzes the appropriateness of parametric testing for outcomes from the cold pressor test (CPT), a common human experimental-pain test. We systematically reviewed published CPT studies to quantify how often researchers test for normality and how often they use parametric vs. non-parametric tests. We then measured the normality of CPT data from 7 independent small-to-medium cohorts and one study of >10,000 subjects. We then examined the ability of two common mathematical transformations to normalize our skewed data-sets. Lastly, we performed Monte Carlo simulations on a representative dataset to compare the statistical power of the parametric t-test vs. the nonparametric Wilcoxon Mann Whitney (WMW) test. We found that only 39% of published CPT studies (47/122) mentioned checking data distribution, yet 72% (88/122) used parametric statistics. Furthermore, among our 8 data sets, CPT outcomes were virtually always non-normally distributed and mathematical transformations were largely ineffective in normalizing them. The simulations demonstrated that the non-parametric WMW test had greater statistical power than the parametric t-test for all scenarios tested– for small effect sizes, the WMW had up to 300% more power.