Randomization, Replication, Blocking

Derives what each of Fisher's three design principles actually buys an estimator, in the language of bias and variance rather than just definitions: randomization's exact unbiasedness proof under Neyman's finite-population model, why replication is what makes an error variance estimable at all (and why the usual variance estimator is conservative, not exact), and blocking's relative-efficiency payoff from moving nuisance variation out of the error term. Ties all three together in a single Monte Carlo simulation comparing a confounded design, a completely randomized design, and a randomized block design on the same field.