The utility of analytical chemistry measurements in most applications is dependent on an assessment of measurement error. This paper demonstrates the use of a two-component error model in setting limits of detection and related concepts and introduces two goodness-of-fit statistics for assessing the appropriateness of the model for the data at hand. The model is applicable to analytical methods in which high concentrations,are measured with approximately constant relative standard deviation. At low levels, the relative standard deviation cannot stay consistant, since this implies vanishingly small absolute standard deviation. The two-component model has approximately constant standard deviation near zero concentration, and approximately constant relative standard deviation at high concentrations, a pattern that is frequently observed in practice. Here we discuss several important applications of the model to environmental monitoring and also introduce two goodness-of -fit statistics, to ascertain whether the data exhibit the error structure assumed by the model, as well as to look for problems with experimental design.

The use of analytical chemistry measurements in environmental monitoring is dependent on an assessment of measurement error. Models for variation in measurements are needed to quantify uncertainty in measurements, set limits of detection, and preprocess data for more sophisticated analysis in prediction, classification, and clustering. This article explains how a two-component error model can be used to accomplish all of these objectives. In addition, we present applications to quantitating biomarkers of exposure to toxic substances using gene expression microarrays.