BCPS Non-clinical Practice Test

Interaction between two factors is what a factorial design with two factors looks to detect. Two-way ANOVA is built to test not only the independent effects of each factor but also whether one factor’s effect depends on the level of the other—i.e., the interaction. In a scenario with age group and treatment, two-way ANOVA asks: does the treatment effect vary across age groups? If you find a significant interaction, it means the treatment works differently depending on age, so you’d examine simple effects (the treatment effect within each age group) to understand exactly where differences lie. The main effects tell you overall differences by age and by treatment, but only the interaction reveals dependence of one factor’s effect on the other. One-way ANOVA only handles a single factor, so it can’t assess how two factors influence each other. Repeated measures ANOVA handles within-subjects or repeated measurements, not a separate between-subjects interaction of two factors, unless the design explicitly treats one factor as within-subject. Thus, for analyzing the interaction between age group and treatment, two-way ANOVA is the appropriate choice.