This episode introduces the core logic of experimental design and ANOVA: what we mean by causality, factors, and confounders—and why randomization, replication, and blocking are the practical tools that make comparisons fair.
We build the one-way ANOVA model, run the hypothesis test in R, and discuss multiple comparisons and how to control Type I error.
We also connect ANOVA to regression, highlight R.A. Fisher’s role in modern statistics, and close with randomized block designs to improve precision by accounting for nuisance variation.
