When many (m) null hypotheses are tested with a single dataset, the control of the number of false rejections is often the principal consideration. Two popular controlling rates are the probability of making at least one false discovery (FWER) and the expected fraction of false discoveries among all rejections (FDR). Scaled multiple comparison error rates form a new family that bridges the gap between these two extremes. For example, the Scaled Expected Value (SEV) limits the number of false positives relative to an arbitrary increasing function of the number of rejections, that is, E(FP/s(R)). We discuss the problem of how to choose in practice which procedure to use, with elements of an optimality theory, by considering the number of false rejections FP separately from the number of correct rejections TP. Using this framework we will show how to choose an element in the new family mentioned above.