5 - Analyzing Non-normally Distributed, Continuous Data: Non-parametric Tests

Summary

Again, parametric procedures are preferred over non-parametric because parametric analyses are more robust in that they use the actual values of the distribution in the analysis. If the data are incapable of becoming “normalized” by transforming the distribution to approximate a normal distribution, such as taking log₁₀ of all HIV viral load values, non-parametric tests should be applied to examine your data. Let’s examine some non-parametric approaches to analyzing non-normally distributed data. In general, two tests, the Mann–Whitney U test and Spearman rank test, fall into this analytic category. In short, the Mann–Whitney U test is the non-parametric equivalent to the T-test and the Spearman rank test is the non-parametric equivalent to the Pearson correlation.