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A brief guide to measurement uncertainty (IUPAC Technical Report)
National Institute of Standards and Technology, Gaithersburg MD, USA; Georgetown University, Washington DC, USA.
School of Chemistry, UNSW, Sydney NSW, Australia.
Institute of Chemistry and Biotechnology, Zürich University of Applied Sciences, Wädenswil, Switzerland.
Örebro University, Örebro University School of Business. National Institute of Standards and Technology, Gaithersburg MD, USA.ORCID iD: 0000-0003-1359-3311
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2024 (English)In: Pure and Applied Chemistry, ISSN 0033-4545, E-ISSN 1365-3075, Vol. 96, no 1, p. 113-134Article in journal (Refereed) Published
Abstract [en]

This Brief Guide reintroduces readers to the main concepts and technical tools used for the evaluation and expression of measurement uncertainty, including both classical and Bayesian statistical methods. The general approach is the same that was adopted by the Guide to the Expression of Uncertainty in Measurement (GUM): quantities whose values are surrounded by uncertainty are modeled as random variables, which enables the application of a wide range of techniques from probability and statistics to the evaluation of measurement uncertainty. All the methods presented are illustrated with examples involving real measurement results from a wide range of fields of chemistry and related sciences, ranging from classical analytical chemistry as practiced at the beginning to the 20th century, to contemporary studies of isotopic compositions of the elements and clinical trials. The supplementary material offers profusely annotated computer codes that allow the readers to reproduce all the calculations underlying the results presented in the examples.

Place, publisher, year, edition, pages
Walter de Gruyter, 2024. Vol. 96, no 1, p. 113-134
Keywords [en]
Bayesian methods, Gauss's formula, measurement uncertainty, Monte Carlo methods, uncertainty propagation
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:oru:diva-111333DOI: 10.1515/pac-2022-1203ISI: 001144906700001Scopus ID: 2-s2.0-85182977246OAI: oai:DiVA.org:oru-111333DiVA, id: diva2:1834120
Available from: 2024-02-02 Created: 2024-02-02 Last updated: 2024-06-17Bibliographically approved

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Bodnar, Olha

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