  This paper contains two finite-sample results about the sign test. First, we
show that the sign test is unbiased against two-sided alternatives even when
observations are not identically distributed. Second, we provide simple
theoretical counterexamples to show that correlation that is unaccounted for
leads to size distortion and over-rejection. Our results have implication for
practitioners, who are increasingly employing randomization tests for
inference.
