Using validation data for ISO measurement uncertainty estimationPart 1. Principles of an approach using cause and effect analysis

Abstract

A strategy for reconciling the information requirements of formal measurement uncertainty estimation principles with data generated from classical analytical method validation studies is described in detail. The approach involves a detailed analysis of influence factors on the analytical results, employing cause and effect analysis, followed by a formal reconciliation stage. The methodology is shown to be consistent with the principles outlined in the ISO Guide to the Expression of Uncertainty in Measurement (GUM), given representative data. Any relevant data may be used, including those obtained from classical validation studies. The relationship between classical validation studies and ISO GUM uncertainty estimation is discussed briefly; it is concluded that the two methodologies are equivalent, subject to additional allowance for terms held constant during validation experiments.