This chapter deals with either clustering, where every individual within each cluster has the same treatment, or stratification, where there are individuals with different treatments within each stratum. For the studies with clustering, we compare two individual-level analysis methods (generalized estimating equations and random effects models), and a cluster-level analysis (performing a t-test on the means from each cluster). We simulate cluster analyses when the effect is or is not related to cluster size. In the stratification context, we explore Simpson’s paradox, where the direction of the within stratum effects is different from the direction of the overall effect. We show the appropriate analysis of data that are consistent with Simpson’s paradox should adjust for the strata or not depending on the study design. We discuss the stratification adjusted tests of Mantel and Haenszel, van Elteren, and quasi-likelihood binomial or Poisson models. We compare meta-analysis using fixed effects or random effects (e.g., Dersimonian–Laird method). Finally, we describe confidence intervals for directly standardized rates.