[en] This paper describes the use of a new figure of merit code that calculates both bias and precision for coincidence and multiplicity counting, and determines the optimum regions for each in waste assay applications. A tunable multiplicity approach is developed that uses a combination of coincidence and multiplicity counting to minimize the total assay error. An example is shown where multiplicity analysis is used to solve for mass, alpha, and multiplication and tunable multiplicity is shown to work well. The approach provides a method for selecting coincidence, multiplicity, or tunable multiplicity counting to give the best assay with the lowest total error over a broad spectrum of assay conditions