What characterizes a nonparametric test?

A nonparametric test is characterized primarily by its application when the assumptions required for parametric tests are not met. Parametric tests typically rely on specific assumptions about the data distribution, such as normality and homogeneity of variance. When these conditions are violated or when dealing with ordinal data or nominal data, nonparametric tests become appropriate alternatives because they do not make stringent assumptions regarding the underlying distribution of the data.

Unlike parametric tests, nonparametric tests can analyze data that is not normally distributed or that is measured on a lower scale (such as ordinal). This flexibility allows researchers to apply these tests in a wider variety of situations. Additionally, nonparametric tests can process ordinal data, which does not require interval-level measurements, making them suitable for data types that parametric tests cannot adequately address.

The other options mention features that align with parametric tests or suggest misunderstandings about the nature of nonparametric statistics. For instance, nonparametric tests do not assume specific distributions, nor do they require interval data. Furthermore, they are not the same as a t-test, which is a type of parametric test. Hence, the correct characterization of a nonparametric test is that it is specifically designed for scenarios where the assumptions of param