This episode moves from one-way ANOVA to two-factor randomized experiments, focusing on how to test main effects and, more importantly, interactions—when the effect of one factor depends on the level of the other.
Using examples like printer sales and a fish reproduction index, we show how ANOVA partitions variation and supports hypothesis testing. We also give a quick tour of extensions including random-effects and mixed-effects models, plus ANCOVA for adjusting with covariates.
In the second half, we shift to study design in biomedicine—contrasting prospective vs. retrospective data collection—and close with a short introduction to categorical data analysis and basic concepts in clinical trials.
