A multiparameter, programmable model was developed to examine the interactive influence of certain parameters on the probability of deciding that an examinee had attained a specified degree of mastery. It was applied within the simulated context of performance testing of military trainees. These parameters included: (1) the number of assumed mastery states--master, nonmaster, and perhaps intermediate (likely to soon achieve mastery); (2) the prior distribution of scores from similar examinee groups; and (3) the number of test trials or items administered. The results of several simulations showed that the degree of confidence that a decisionmaker can have about the testee's mastery is markedly affected by the values for the three parameters, and the effects of their combination. Using the Bayesian model, test length and costs could be reduced--as long as the prior information was accurate and valid for the particular group of examinees. Results of the simulation also showed that a test may be too short to be of decision-making value. (Author/GDC)