nomic analysis, classical hypothesis testing, Bayes methods, or the like—a test has (statistical) operating characteristics. These operating characteristics are the probabilities of making various decisions based on underlying properties of the system under test. (The operating characteristics of a simple hypothesis test are the probability of rejecting the null hypothesis when it is true and the probability of failure to reject the null hypothesis when it is false.) Generalizing the notion of operating characteristics would provide the correct basis for decision rules; for example, a decision rule based on a specific test design would ideally have a high probability of passing a system that met the requirement and a high probability of failing a system that did not meet the requirement. Estimates of the operating characteristics of a test should be communicated to decision makers and recognized in the decision process. Finally, a decision rule could be enriched through use of information from such sources as simulations and developmental test results (a relevant paper is Fries and Easterling, 2002).