What defines the data in nonparametric statistical tests?

Nonparametric statistical tests are characterized by their ability to handle data that does not adhere to specific assumptions about the underlying population distribution. While the term "discrete data" might seem related, the key aspect of nonparametric tests is their flexibility regarding the type of data they can analyze.

Nonparametric tests are particularly useful for analyzing ordinal data, ranked data, or data that is not normally distributed. Therefore, they can accommodate both discrete and continuous data, provided that the data does not fit the typical parametric assumptions, such as being normally distributed or having a fixed mean.

The option that represents the defining feature of nonparametric tests is actually the flexibility to work with various types of data, rather than being restricted solely to discrete data. So while nonparametric tests can indeed handle discrete data, this is not specific enough to fully capture their essence.

Using nonparametric tests, researchers can analyze data that do not conform to the assumptions required by parametric tests, making them a valuable tool in statistical analysis.