This chapter introduces conventional and permutation methods for multiple matched samples, i.e., randomized-blocks designs. The chapter contains example analyses illustrating the computation of exact permutation probability values for randomized-blocks designs, calculation of measures of effect size for randomized-blocks designs, exact and Monte Carlo permutation procedures for randomized-blocks designs, and applications of permutation methods to randomized-blocks designs with rank-score data. Also included in the chapter are permutation versions of Fisher’s F test for a one-way randomized-blocks design, Friedman’s two-way analysis of variance for ranks, and a permutation-based alternative for the four conventional measures of effect size for randomized-blocks designs: Hays’ \(\hat{\omega}^{2}\) , Pearson’s \(\eta ^{2}\) , Cohen’s partial \(\eta ^{2}\) , and Cohen’s \(f^{2}\) .