2.5. Data Processing and Statistical Analyses
Statistical analyses were performed using JMP v.14 (the SAS software package). Data distributions were viewed by examining the normal quantile plots and histograms, and normality was assessed using the Shapiro–Wilk W test (a goodness-of-fit test). Study variables are expressed as mean ± SD unless otherwise indicated, whereas VAS data that were not normally distributed were presented as the geometric mean ± SD. Variables that were not normally distributed were subjected to non-parametric statistical analysis. We used a non-parametric repeated measure Friedman test to assess the differences in peripheral skin temperature and skin blood flow scores across three conditions: high dose, low dose, and placebo. The investigation hypothesis point was whether there would be a significant difference in peripheral skin temperature and skin blood flow scores between the three conditions. The Friedman test is a non-parametric alternative to repeated-measures ANOVA, which is recommended to be used to analyze related data when the dependent variable is measured on an ordinal scale. It is a non-parametric test, which entails that it does not assume that the data are normally distributed or have equal variances. The significance level was set a priori at 