The combination of the Wilcoxon-Mann-Whitney test with the statistical programming language R offers a robust method for comparing two independent groups when the data are not normally distributed or when the assumption of equal variances is violated. This non-parametric test, implemented via R’s statistical functions, assesses whether two samples are likely to derive from the same population. For example, this approach can evaluate if the recovery times differ significantly between patients receiving two different treatments, using the rank ordering of the observed recovery times instead of their raw values.
The utility of this combination lies in its flexibility and accessibility. R provides a versatile environment for conducting statistical analyses, including the aforementioned test, and producing informative visualizations. This allows researchers to efficiently explore their data, perform appropriate statistical inference when parametric assumptions are untenable, and effectively communicate their findings. Historically, researchers relied on manual calculations or specialized software; however, R’s open-source nature and extensive libraries have democratized access to such analytical tools, making it readily available for a broad audience.
