Medical College Admission Test (MCAT) Psychological, Social, and Biological Foundations of Behavior (Psych/Soc) 2025 – 400 Free Practice Questions to Pass the Exam

The best method for comparing mean values of a continuous variable across multiple categories is ANOVA (Analysis of Variance). ANOVA is specifically designed to analyze differences among group means in a sample. It assesses whether there are statistically significant differences between the means of three or more independent groups.

When dealing with multiple categories, conducting multiple T-tests (which compare the means between two groups) increases the risk of Type I error, which is the incorrect rejection of a true null hypothesis. ANOVA offers a more efficient and powerful approach, as it evaluates all groups simultaneously while controlling for the overall error rate.

In addition, the Chi-square test is typically used for categorical data to examine the association between two categorical variables rather than mean values of a continuous variable. Internal validity pertains to the extent to which an experiment accurately establishes a causal relationship, but it does not serve as a statistical method for comparing means.

Thus, ANOVA is the most appropriate choice for the situation described, allowing researchers to effectively compare mean values across multiple categories without the drawbacks associated with other methods.