What are nonparametric tests used for in statistics?

Nonparametric tests are particularly valuable in statistics because they allow for the analysis of data that do not meet the assumptions required for parametric tests, such as normal distribution. This includes ordinal or nominal data, which are types of categorical data that often do not have a defined scale or that do not conform to a normal distribution.

Nonparametric methods are versatile and robust, making them ideal for data that is skewed, has outliers, or does not follow a specific distribution pattern. By not relying on the characteristics of a normal distribution, these tests enable researchers to draw conclusions from data sets that may otherwise be excluded from analysis. This flexibility is particularly useful in real-world data analysis, where many variables do not meet parametric test requirements.

The other options are not aligned with the primary purpose of nonparametric tests. Analyzing data assuming a normal distribution pertains to parametric tests, while creating detailed quantitative analyses is often associated with techniques that require specific distribution assumptions. Similarly, validating findings from parametric tests usually involves other parametric methodologies rather than nonparametric approaches, further emphasizing the distinct role of nonparametric tests in statistical analysis.