The Analysis of Variance (ANOVA) is a statistical technique widely used in medical research to evaluate the effect of therapeutic interventions. This method enables researchers to determine whether there are statistically significant differences between treatment groups, accounting for change within and between groups to isolate treatment effects. In medical studies, ANOVA is particularly useful for comparing multiple intervention groups, examining interactions between variables (e.g., treatment type and dosage), and tracking changes over time in longitudinal research. This paper reviews the application of different ANOVA types, such as one-way, two-way, and repeated measures ANOVA, and highlights their specific contributions to understanding therapeutic efficacy and patient outcomes. Additionally, the study explores the methodological assumptions necessary for ANOVA, such as normality, homogeneity of variances, and independence, while discussing alternatives and adaptations for non-ideal data conditions. Through case studies and examples, we demonstrate how ANOVA facilitates evidence-based assessments of treatment differences, providing a rigorous foundation for decision-making in clinical and pharmacological research. Ultimately, ANOVA's systematic approach to variance analysis positions it as an essential tool in medical research, supporting more accurate and comprehensive evaluations of therapeutic interventions.