  We discuss a method for correcting the bias in the limits for small signals
if those limits were found based on cuts that were chosen by minimizing a
criterion such as sensitivity. Such a bias is commonly present when a
"minimization" and an "evaluation" are done at the same time. We propose to use
a variant of the bootstrap to adjust the limits. A Monte Carlo study shows that
these new limits have correct coverage.
