What type of data is most suitable for nonparametric tests?

Nonparametric tests are particularly well-suited for analyzing ordinal data or data that does not follow a normal distribution. This is primarily because nonparametric methods make fewer assumptions about the underlying population distributions. While parametric tests rely heavily on the stipulation that the data has a normal distribution and meets specific variances, nonparametric tests can handle data that is ranked or that may have inconsistent distributions.

Using ordinal data is a key characteristic of nonparametric tests, as ordinal data reflects categories with a meaningful order but without consistent intervals between them. For example, survey responses such as "satisfied," "neutral," and "dissatisfied" can be explored using nonparametric methodologies. Additionally, nonparametric tests are beneficial when dealing with non-normally distributed continuous data, which may not adhere to the assumptions necessary for parametric tests.

In contrast, other types of data mentioned in the options, such as normally distributed data or large sample sizes, are typically better suited to parametric tests, which yield more powerful statistical conclusions. Therefore, option B highlights the essential characteristic of nonparametric tests, making it the ideal choice.