How do parametric and non-parametric tests differ?

Parametric and non-parametric tests differ fundamentally in their assumptions about the data being analyzed. Parametric tests require assumptions regarding the distribution of the data, typically assuming that the data follow a normal distribution and that certain parameters (like the mean and standard deviation) are applicable. These tests can provide more powerful statistical inferences when the data meet these assumptions.

In contrast, non-parametric tests do not make such stringent assumptions about the data's distribution and can be used in cases where the data do not meet the criteria required for parametric tests. This flexibility allows non-parametric tests to be applied to a wider range of data types, including ordinal and non-normally distributed interval data. As a result, option C accurately highlights the distinguishing feature of parametric tests in reference to their reliance on distributional characteristics.