What type of statistical test is best for comparing summarized data from two or more samples?

The best choice for comparing summarized data from two or more samples is ANOVA, which stands for Analysis of Variance. This statistical test is specifically designed to assess the differences in means among multiple groups. When you have more than two samples and want to determine if at least one group mean is different from the others, ANOVA is the appropriate method to use. It allows for the simultaneous comparison of multiple group means and can help identify if any significant differences exist across groups.

ANOVA operates under the assumption that the samples are normally distributed, have homogeneous variances, and are independent of each other, making it robust for analyzing data in various practical scenarios, such as experimental studies or observational research. It helps control for the Type I error that may occur if multiple t-tests were conducted instead.

In contrast, while a t-test is suitable for comparing the means from exactly two groups, and the chi-square test is mainly used for categorical data to examine relationships between groups, regression analysis focuses on modeling relationships between variables rather than directly comparing group means. Consequently, ANOVA is the most suitable statistical test for comparing summarized data across multiple samples.