Final answer:
ANOVA tests for differences in group means while Chi Square tests for differences in categorical distributions or independence of variables.
Step-by-step explanation:
Analysis of Variance (ANOVA) allows us to test whether there is a difference in means between groups, whereas Chi Square Analysis allows us to test whether there is a difference in categorical distributions or the independence of categorical variables.
Analysis of Variance (ANOVA)
One-Way ANOVA hypothesis testing determines if several population means are equal by comparing the means of a response variable for several groups. It evaluates the null hypothesis that all group means are equal against the alternative hypothesis that at least one group mean is different. It uses the F distribution with degrees of freedom defined by the number of groups and the number of observations.
Chi Square Analysis
Chi Square Analysis includes various tests such as the goodness-of-fit test, test of independence, and test of homogeneity. These tests assess whether observed frequencies match expected frequencies, whether two categorical variables are independent, or whether two samples come from the same distribution.