In a two-way ANOVA, what additional information does it provide beyond main effects?

• A. It tests interaction between factors.
• B. It increases sample size automatically.
• C. It tests normality of residuals.
• D. It tests equality of variances.

Answer: (A)

Two-way ANOVA adds information about whether the effect of one factor depends on the level of the other factor. This is the interaction between factors: it tells you if the factors combine in a way that their combined effect cannot be explained by simply adding their separate (main) effects. For example, the difference between groups on one factor might be large at one level of the other factor and small at another, which shows up as a significant interaction. When an interaction is present, you interpret the effects in the context of that interaction rather than by looking at main effects in isolation. If there’s no interaction, the main effects give a clearer picture of each factor’s independent influence. The other options aren’t provided by the ANOVA output itself: sample size is determined by study design, and checks for normality of residuals or equality of variances are assumption checks (often done with separate tests or plots) rather than features reported by the two-way ANOVA.