Nonparametric methods are valuable when assumptions are violated from "summary" of Statistics for Censored Environmental Data Using Minitab and R by Dennis R. Helsel
Nonparametric methods are valuable when assumptions are violated because they do not rely on specific assumptions about the underlying distribution of the data. In traditional parametric methods, such as t-tests or ANOVA, assumptions about normality, homogeneity of variances, and linearity must be met in order for the results to be valid. However, in many real-world situations, these assumptions are often violated, leading to biased or inaccurate results. Nonparametric methods, on the other hand, are distribution-free and do not make any assumptions about the shape or parameters of the underlying distribution. This makes them more robust and reliable when dealing with data that do not meet ...
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