  We consider the general performance of the difference-in-means estimator in
an equally-allocated two-arm randomized experiment under common experimental
endpoints such as continuous (regression), incidence, proportion, count and
uncensored survival. We consider two sources of randomness: the
subject-specific assignments and the contribution of unobserved
subject-specific measurements. We then examine mean squared error (MSE)
performance under a new, more realistic "simultaneous tail criterion". We prove
that the pairwise matching design of Greevy et al. (2004) performs best
asymptotically under this criterion when compared to other blocking designs. We
also prove that the optimal design must be less random than complete
randomization and more random than any deterministic, optimized allocation.
Theoretical results are supported by simulations in all five response types.
