Nonparametric tests such as the Kruskal-Wallis are beneficial because they:

Nonparametric tests, like the Kruskal-Wallis test, are advantageous primarily because they require fewer assumptions regarding the underlying data compared to parametric tests. Specifically, nonparametric tests do not assume that the data follows a normal distribution, which is a critical assumption for many parametric tests. This flexibility allows researchers to apply nonparametric methods in a wider variety of situations where data may be skewed, ordinal, or not meet other parametric test assumptions.

Additionally, the assumptions made by nonparametric tests typically involve less stringent conditions about the scale of measurement, making them appropriate for analyzing data that may not adhere to the properties of interval or ratio scales used in parametric testing. This quality makes nonparametric tests valuable in practical application, especially in fields where data can be irregular or non-typical.

While some nonparametric tests may handle missing data better than others might, that is not the primary reason for their utility. Moreover, nonparametric tests are not necessarily more powerful than parametric tests; their power can actually be lower under certain circumstances where parametric tests are applicable. Hence, the reduced requirement for assumptions is what truly highlights the relevance of the Kruskal-Wallis and similar nonparametric tests in