What type of statistical tests are used to compare means across different groups?

The use of statistical tests to compare means across different groups is primarily associated with ANOVA (Analysis of Variance). ANOVA is specifically designed to assess differences in the means of three or more groups, making it particularly powerful for comparing multiple group means simultaneously while controlling the overall error rate.

When using ANOVA, it determines whether there are statistically significant differences between the means of the groups being compared. It does this by analyzing the variance within each group and between the groups, which helps to ascertain whether any of the observed differences in means could be attributed to random chance alone.

In contrast, while a T-test can compare means between two groups, it is not suitable when the focus is on three or more groups. The F-test is not a standalone test for means but rather a term often used in the context of ANOVA, as it generates an F-statistic to assess the variance differences. The Chi-square test is a non-parametric test used to compare categorical data rather than means, making it inappropriate for this context.