Improved attribute acceptance sampling plans in the presence of misclassification error

This paper considers an attribute acceptance sampling problem in which inspection errors can occur. Unlike many common situations, the source of the inspection errors is the uncertainty associated with statistical sampling. Consider a lot that consists of N containers, with each container including a large number of units. It is desired to sample some of the containers and inspect a sample of units from these selected containers to determine proper disposition of the entire lot. Results presented in the paper demonstrate significant shortcomings in traditional sampling plans when applied to this context. Alternative sampling plans designed to address the risk of statistical classification error are presented. These plans estimate the rate of classification errors and set plan parameters to reduce the potential impact of such errors. Results are provided comparing traditional plans with the proposed alternatives. Limitations of the new plans are also discussed.