The study approach reduced from 100 percent the amount of testing which had to be done in order to monitor the use of illegal drugs in the population and provided a decision rule vis- a-vis a sampling plan that specified under what sampling outcome the entire population was to be tested. The objective was to minimize the expected total cost of a drug testing program while ensuring that the proportion of users in the population did not increase over time. A field study of the Bayesian sampling approach was conducted using probationers assigned to intensive drug supervision programs in six Illinois counties. The degree to which drug testing results were reported to probation officers was controlled during the experiment. Counties were assigned to receive no feedback of drug test results, random proportion of feedback using Bayesian acceptance sampling plans, or 100 percent feedback (status quo situation). Results showed that counties using Bayesian acceptance sampling could have reduced the number of drug tests performed without increasing drug use. In counties with no feedback, an upward trend was observed over time in the proportion of probationers testing positive for drug use. The authors indicate that Bayesian acceptance sampling-based drug testing programs are now being implemented by Illinois probation offices. The cost assessment protocol used in the study is appended. 27 references, 6 tables, and 4 figures