Techniques such as X-Ray Fluorescence (XRF) spectroscopy invariably provide the practitioner with assay data for a large number M of different species. During this investigation, the question arose as to whether the error amplification factors given by Equation 10 could be used in some simple way to combine the yield values calculated from these M different species to provide an accurate estimate of the true yield. However, the problem in practice with using Equation 11 (or 12) to provide the standard errors to use in Equation 20 is that Equation 11 (or 12) requires one to know a priori the true yield and recoveries of all the different species (or the true feed and tailings grades). So, one cannot accurately estimate the uncertainty of the yield values calculated from the assay data of each species, particularly those close to the singularities. We used Monte Carlo simulations to assess the accuracy of various schemes to estimate the yield based on the variability across all the assay data. However, we did not find any simple approach that was accurate, and the more complex approaches offered no computational advantage over conventional massbalancing of the raw assay data, which also has the benefit of providing estimates of the true assays and recoveries (not just the true yield).