Analysis of Variance (ANOVA) forms a critical link between experimental design and statistical inference, and this chapter offers an in-depth look at its theoretical foundations and practical applications. Beginning with the Completely Randomized Design , the chapter introduces the partitioning of variance and the F-test, using intuitive visualizations and algebraic expressions. Advanced designs such as randomized blocks and nested designs are then developed, followed by nonparametric approaches that relax assumptions about normality and homogeneity. The mathematics behind ANOVA is derived in detail, including the expected mean squares and orthogonal decomposition of variance. Emphasis is placed on correct interpretation of p-values and effect sizes. The chapter concludes with guidance on sample size planning and the introduction of covariance models.