What type of statistical analysis is used to compare the means of more than two groups?

The correct choice is ANOVA, which stands for Analysis of Variance. This statistical method is specifically designed to test for significant differences between the means of three or more groups. ANOVA assesses whether any of those means are significantly different from each other by examining the variance within the groups compared to the variance between the groups.

When comparing means across multiple groups, simply using multiple t-tests can increase the likelihood of Type I errors, which is why ANOVA is preferred for this purpose. It provides a robust framework for analyzing group means simultaneously while controlling for this error risk.

On the other hand, Chi-square tests are used for categorical data to examine the relationships between two or more groups, but they do not compare means. Regression analysis is used for examining relationships between variables, typically involving a dependent and one or more independent variables, rather than directly comparing group means. A t-test is used for comparing the means between two groups only, making it unsuitable for analyzing scenarios with more than two groups. Thus, ANOVA is the appropriate choice when the goal is to compare the means of three or more groups.