ENBIS-8 in Athens

21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008

Revision of the Guide to the Expression of Uncertainty in Measurement

23 September 2008, 12:40 – 13:00

Abstract

Submitted by
Alistair Forbes
Authors
Alistair Forbes
Affiliation
National Physical Laboratory
Abstract
Since its publication by ISO in 1995, the Guide to the Expression of Uncertainty in Measurement (GUM) has been successful in fostering a more coherent and probabilistic basis for reporting measurement uncertainty and it has been adopted by National Metrology Institutes and measurement laboratories throughout the world. The GUM does have its critics, some pointing to inconsistencies, others concerned with its perceived narrow scope or presentational issues. In order to broaden the scope, supporting documents are being prepared on various aspects, including multivariate measurands, conformance to specification and the use of Monte Carlo methods in propagating probability distributions, the latter document recently published as GUM Supplement 1.

However, the GUM document itself is undergoing revision and it remains to be seen how far reaching the revision will be. The GUM, as it stands, can be seen as giving guidance on how to characterise the accuracy of a measurement system. Given a measurand (such as the length of a gauge block) it aims to describe the spread of results that could be expected for such a measurement system, due to various influence factors (such as temperature). Measurement system characterisation can be regarded as a type of forward uncertainty evaluation, from influence factors to measurement result. Users of measurement systems are interested in another question: given that the measurement system has produced the measured data, what can be said of the likely value of the measurand. For measurement systems with linear responses, inferences about the measurand can be made directly from the measurement system characterisation. For systems with nonlinear characteristics, the passage from measurement system characterisation to inferences about the measurand requires a type of inverse uncertainty evaluation, usually achieved through the application of Bayes’ theorem. The extent of the revision of the GUM will depend on to what extent the revised version encompasses both forward and inverse uncertainty evaluation. In this paper, we illustrate some of the issues in forward and inverse uncertainty evaluation for alignment errors in length metrology.

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