In which scenario would a non-parametric test be most appropriate?

A non-parametric test is most appropriate in situations where the data do not meet the assumptions necessary for parametric tests. One key scenario for using non-parametric tests is when dealing with rank or frequency data. This type of data does not rely on the assumption of a normal distribution, making non-parametric tests suitable. Non-parametric tests generally exhibit greater flexibility and can be used for data that are ordinal (ranked) or data that consist of counts (frequency).

For instance, if you are assessing the preferences of a group using rankings or evaluating the occurrence of various categories, a non-parametric test helps in analyzing such data without making strong assumptions about the underlying distribution. This can be particularly useful in cases where the sample size is small or the data show significant deviations from normality.

On the other hand, the other scenarios presented are aligned with situations where parametric tests would typically be more appropriate. Normal distribution is a foundational requirement for many parametric tests, means are generally calculated in parametric contexts, and linear regression relies heavily on several assumptions, including normality, making non-parametric tests unsuitable for these analyses.