Which statistical test is classified as nonparametric and suitable for non-normal populations?

The Chi Square test is a nonparametric statistical test used to determine whether there is a significant association between two categorical variables. It does not assume that the data follows any specific distribution, which makes it particularly suitable for analyzing data from populations that do not meet the assumptions of normality required by parametric tests like ANOVA or the t-test.

In cases where the data does not meet the assumptions of normality, nonparametric tests like the Chi Square can be advantageous because they work with the ranks or frequencies of the data rather than the raw scores. This allows researchers to analyze relationships or differences without being restricted to normally distributed data, making it a versatile tool in statistical analysis, especially in fields like social sciences and medicine where the data often does not conform to normality.

The other options, such as ANOVA and t tests, are parametric tests that rely on the assumption of a normal distribution, while MANOVA extends ANOVA to multiple dependent variables but also assumes normality. Thus, the Chi Square test stands out as the appropriate choice for non-normal populations.