Non-parametric tests are hypothesis testing procedures that assume the variables of interest are measured on nominal or ordinal scales.Select one:TrueFalse

Answer

The statement is True.

Non-parametric tests are indeed designed for data that do not follow a normal distribution or for which the assumptions required for parametric tests (like t-tests) are not met. These tests are particularly useful when dealing with variables that are measured at the nominal or ordinal levels.

Nominal scales categorize data without any order (e.g., types of fruit), while ordinal scales provide a rank order among categories (e.g., satisfaction ratings). Non-parametric tests, such as the Chi-square test, Mann-Whitney U test, and Kruskal-Wallis test, are therefore appropriate tools for hypothesis testing in these contexts. They allow researchers to draw conclusions from the data without making strong assumptions about the underlying distributions.

In summary, non-parametric tests are essential for research involving nominal and ordinal variables, making the initial statement accurate.