scholarly journals How many shades of grey are in conformity assessment due to measurement uncertainty?

2019 ◽  
Vol 1420 ◽  
pp. 012001
Author(s):  
Ilya Kuselman ◽  
Francesca R Pennecchi ◽  
Ricardo J N B da Silva ◽  
D Brynn Hibbert
Proceedings ◽  
2020 ◽  
Vol 55 (1) ◽  
pp. 17
Author(s):  
Stephen L. R. Ellison

Many analytical measurements are made in order to check a system or product for conformity [...]


2004 ◽  
Vol 9 (6) ◽  
pp. 384-384
Author(s):  
H�kan K�llgren ◽  
Margreet Lauwaars ◽  
Bertil Magnusson ◽  
Leslie Pendrill ◽  
Phillip Taylor

2021 ◽  
Vol 854 (1) ◽  
pp. 012093
Author(s):  
Silvana Stajkovic ◽  
Dragan Vasilev ◽  
Mirjana Dimitrijevic ◽  
Nedjeljko Karabasil

Abstract Knowledge of the measurement uncertainty of test results is fundamentally important for laboratories, their customers and all parties using and interpreting these results. In conformity assessment, a measurement result is used to decide if an item of interest conforms to a specified requirement. Because of measurement uncertainty, there is always the risk of incorrectly deciding whether or not an item conforms to a specified requirement based on the measured value of a property of the item. Conformity assessment can be quite challenging when the entity measured is so close to the tolerance limits of the specification that its uncertainty, however estimated, critically affects decision-making. In such cases, different decision rules can be used to make statements of conformity. The aim of this paper is to provide a survey of methods for the evaluation of measurement uncertainty in testing, as well as to stress the need for appropriate estimation of measurement uncertainty. This paper also aims to assist testing laboratories in understanding the different decision rules used in conformity assessment and level of risk (such as false accept and false reject) associated with the decision rule employed.


Author(s):  
Alexandre Allard ◽  
Nicolas Fischer ◽  
Ian Smith ◽  
Peter Harris ◽  
Leslie Pendrill

In 2012, the Joint Committee for Guides in Metrology (JCGM) published novel guidance on the consideration of measurement uncertainty for decision-making in conformity assessment (JCGM 106:2012). The two situations of making a wrong decision are considered: the risk of accepting a non-conforming item, denoted as the customer risk, and the risk of rejecting a conforming item, denoted as the producer risk. In 2017, the revision of ISO 17025 obliged calibration and testing laboratories to “document the decision rule employed, taking into account the level of risk (such as false accept and false reject and statistical assumptions) associated with the decision rule employed, and apply the decision rule” in the context of the decision made about the conformity of an item. However, JCGM 106:2012 can in some cases be perceived as quite difficult to apply for non-statisticians as it mainly relies on calculations involving probability distributions. In order to facilitate uptake of the methodology of JCGM 106:2012, EURAMET is funding the project EMPIR 17SIP05 “CASoft” (2018 – 2020), involving the National Measurement Institutes from France, Sweden and the UK. The objective is to make the methodology accessible to organisations involved in decision-making in conformity assessment: calibration and testing laboratories, industrialists and regulation authorities. Where the customer or producer are concerned, there are two kinds of risks arising from measurement uncertainty: specific risk which concerns the risk of an incorrect decision for a particular item and global risk which is the risk of an incorrect decision for any item chosen at random. Both kinds of risk may involve prior information, taken into account through a so-called prior probability distribution, introducing the concept of a Bayesian evaluation of the risks. If a calibration and testing laboratory performing the measurement has difficulty accessing prior information, it is likely that the industrialist in control of production processes will have some idea of the quality of the items produced. In this paper, the two problems of estimating the specific and global risks are addressed. The consideration of prior information is also discussed through a practical example as well as the use of software implementing the methodology, which will be made publically available at the end of the project.


2017 ◽  
Vol 131 ◽  
pp. 79-91 ◽  
Author(s):  
Luciano Molognoni ◽  
Leandro Antunes de Sá Ploêncio ◽  
Antonio Marcelo Lemos Machado ◽  
Heitor Daguer

Food Control ◽  
2021 ◽  
Vol 125 ◽  
pp. 107949
Author(s):  
Francesca R. Pennecchi ◽  
Ilya Kuselman ◽  
Aglaia Di Rocco ◽  
D. Brynn Hibbert ◽  
Anastasia A. Semenova

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