  We consider the problem of choosing the best of $n$ samples, out of a large
random pool, when the sampling of each member is associated with a certain
cost. The quality (worth) of the best sample clearly increases with $n$, but so
do the sampling costs, and one important question is how many to sample for
optimal gain (worth minus costs). If, in addition, the assessment of worth for
each sample is associated with some "measurement error," the perceived best out
of $n$ might not be the actual best, complicating the issue. Situations like
this are typical in mate selection, job hiring, and food foraging, to name just
a few. We tackle the problem by standard order statistics, yielding suggestions
for optimal strategies, as well as some unexpected insights.
