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

Stephen L. R. Ellison

The Analyst · 1998 · 93 citations · 1 references

Concepts

TL;DR

Classical validation studies and ISO GUM uncertainty estimation are equivalent, provided additional allowance for terms held constant during validation experiments. The study proposes a strategy to reconcile formal measurement uncertainty estimation principles with data from classical analytical method validation studies. The strategy employs cause‑and‑effect analysis of influence factors on analytical results, followed by a formal reconciliation stage, and can use any relevant data, including classical validation study data. The methodology is consistent with ISO GUM principles given representative data, and the equivalence between classical validation studies and ISO GUM uncertainty estimation is confirmed, subject to allowance for constant terms during validation experiments.

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.

References

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