Optimized Measurement Uncertainty and Decision-Making

2010 ◽  
pp. 423-432
Author(s):  
L. R. Pendrill
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.


2019 ◽  
Vol 116 (32) ◽  
pp. 15985-15990 ◽  
Author(s):  
Milad Memarzadeh ◽  
Gregory L. Britten ◽  
Boris Worm ◽  
Carl Boettiger

Current and future prospects for successfully rebuilding global fisheries remain debated due to uncertain stock status, variable management success, and disruptive environmental change. While scientists routinely account for some of this uncertainty in population models, the mechanisms by which this translates into decision-making and policy are problematic and can lead to unintentional overexploitation. Here, we explicitly track the role of measurement uncertainty and environmental variation in the decision-making process for setting catch quotas. Analyzing 109 well-sampled stocks from all oceans, we show that current practices may attain 55% recovery on average, while richer decision methods borrowed from robotics yield 85% recovery of global stocks by midcentury, higher economic returns, and greater robustness to environmental surprises. These results challenge the consensus that global fisheries can be rebuilt by existing approaches alone, while also underscoring that rebuilding stocks may still be achieved by improved decision-making tools that optimally manage this uncertainty.


2022 ◽  
pp. 306-320
Author(s):  
Kavya Jagan ◽  
Peter M. Harris ◽  
Nadia A.S. Smith ◽  
Jarmo Teuho ◽  
Reetta Siekkinen ◽  
...  

2020 ◽  
Author(s):  
Theodora Chatzimichail ◽  
Aristides T. Hatjimihail

Abstract Background Screening and diagnostic tests are used to classify people with and without a disease. Diagnostic accuracy measures are used to evaluate the correctness of a classification in clinical research and practice. Although the correctness of a classification based on a measurand depends on the uncertainty of measurement, there has been limited research on their relation. The objective for this work is to develop an exploratory tool for the relation between diagnostic accuracy measures and measurement uncertainty, as diagnostic accuracy is fundamental to clinical decision making, while measurement uncertainty is critical to quality and risk management in laboratory medicine. Results For this reason, a freely available interactive program has been developed, written in Wolfram Language. The program provides four modules for calculating, optimizing, plotting and comparing various diagnostic accuracy measures and the corresponding risk of diagnostic or screening tests measuring a normally distributed measurand, applied at a single point in time in non-diseased and diseased populations. This is done for differing prevalence of the disease, mean and standard deviation of the measurand, diagnostic threshold, standard measurement uncertainty of the tests and expected loss. The application of the program is illustrated with a case study of glucose measurements in diabetic and non-diabetic populations, that demonstrates the relation between diagnostic accuracy measures and measurement uncertainty. Conclusion The presented interactive program is user-friendly and can be used as a flexible educational and research tool in medical decision making, to explore the relation between diagnostic accuracy measures and measurement uncertainty.


Author(s):  
Claudio De Capua ◽  
Rosario Morello ◽  
Rosario Carbone

In this paper, the authors examine a common issue concerning the influence of measurement uncertainty on decisions. In fact, in some practical applications, it can be necessary to put in comparison measurement data with thresholds and limits. It occurs when the conformity with fixed specifications has to be verified or if warning and alert levels have to be not exceeded. In such a circumstance, to take reliable decisions in presence of uncertainty is a concrete problem. Measurement uncertainty may reasonably be the cause of unreliable decisions. In order to manage properly the uncertainty effect, the authors have developed a decision making procedure based on a methodical approach to measurement uncertainty. In detail, a fuzzy logic algorithm estimates the probability to take a wrong decision because of the uncertainty. Such information is so used in order to optimize the decisional criteria, improving the consistency of the final computing results. Risks and costs associated to the possibility to take a mistaken decision are minimized. Consequently the algorithm singles out the most reliable decision.


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