A variety of study design and statistical methods to account for the clustering of animal
health and production outcomes is outlined. We argue that the relative utility of study design
vs. statistical methods in accounting for cluster effects depends primarily on the objectives of
the study and the amount of prior information available. The statistical methods outlined vary
from simple post-hoc adjustments of test statistics to relatively complex mixture-distribution
models. Methods for normal, binomial and Poisson distributed data are presented. The …