  Suppose X is a frequency vector that follows a central multiple
hyper-geometric distribution, such as arises in random sampling of an
m-category attribute from a finite population without replacement. We show that
the probability that X satisfies some symmetrical, but otherwise arbitrary,
interval constraints in each component decreases as the sample size increases
or decreases away from one-half of the population size.
