Abstract
Bias in an analytical measurement should be estimated and corrected for, but this is not always done. As an alternative to correction, there are a number of methods that increase the expanded uncertainty to take account of bias. All sensible combinations of correcting or enlarging uncertainty for bias, whether considered significant or not, were modeled by a Latin hypercube simulation of 125,000 iterations for a range of bias values. The fraction of results for which the result and its expanded uncertainty contained the true value of a simulated test measurand was used to assess the different methods. The strategy of estimating the bias and always correcting is consistently the best throughout the range of biases. For expansion of the uncertainty when the bias is considered significant is best done by SUMUMax:U(C-test (result)) = ku(c)(C-test (result)) + |delta(run)|, where k is the coverage factor (= 2 for 95% confidence interval), u(c) is the combined standard uncertainty of the measurement and delta(run) is the run bias.