When the assumptions necessary for parametric tests are not met by your data, such as when the data are not normally distributed, the sample size is small, or the measurement scale is ordinal or nominal rather than interval or ratio, you should apply a nonparametric test (Privitera, 2022). When dealing with skewed data or outliers that can skew the results of parametric analysis, nonparametric tests are also helpful. Nonparametric tests are advantageous because they are flexible, make fewer assumptions about the underlying population, and can yield accurate results even when the data is not suitable for more conventional techniques (Privitera, 2022). Nonparametric tests provide a useful substitute for parametric testing in real-world research scenarios where optimal circumstances are rarely guaranteed, even if they could be less effective than parametric tests when assumptions are met (Privitera, 2022)