  Multiple testing plagues many important questions in finance such as fund and
factor selection. We propose a new way to calibrate both Type I and Type II
errors. Next, using a double-bootstrap method, we establish a t-statistic
hurdle that is associated with a specific false discovery rate (e.g., 5%). We
also establish a hurdle that is associated with a certain acceptable ratio of
misses to false discoveries (Type II error scaled by Type I error), which
effectively allows for differential costs of the two types of mistakes.
Evaluating current methods, we find that they lack power to detect
outperforming managers.
