Improved estimates of expanded measurement uncertainty

Measurement uncertainty (MU) is often estimated (sometimes initially as repeatability) using a limited number of single measurement values (n << 30), or of duplicate measurements on duplicated samples, in order to include the contribution from sampling. In cases such as this where the uncertainty estimate is based on limited data, the common use of a coverage factor of 2.0, based on an assumed normal distribution, to give an approximate 95% confidence interval can result in an underestimate of the expanded MU. Where n is much lower than 30 (e.g., n = 8), this can lead to a serious underestimate. More accurate estimation of the coverage factor, for both the classical and robust ANOVA (e.g., in new software RANOVA v4.0) gives more reliable estimates of the expanded MU by calculating coverage factors based on the t-distribution. A case study for nitrate in lettuce shows that the more accurate coverage factor (of similar to 2.3) substantially increases the expanded MU estimates by 13-14%. An improved estimate of expanded measurement uncertainty, when this is calculated using ANOVA results from a nested experimental design, is described. In cases where the number of sampling targets is << 30, more accurate coverage factors are calculated.