Getting started with uncertainty evaluation using the Monte Carlo method in R

• Published in: Accreditation and quality assurance Vol. 26; no. 3; pp. 129 - 141
• Main Authors: Veen, Adriaan M. H. van der, Cox, Maurice G.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Analytical Chemistry; Approximation; Atomic properties; Biochemistry; Calibration; Chemistry; Chemistry and Materials Science; Commercial Law; Ecotoxicology; Estimates; Food Science; General Paper; Marketing; Monte Carlo simulation; Open source software; Propagation; Testing laboratories; Uncertainty; R; Monte Carlo; Measurement uncertainty; Uncertainty propagation; Calibration; GUM; Testing
• ISSN: 0949-1775; 1432-0517
• DOI: 10.1007/s00769-021-01469-5

The evaluation of measurement uncertainty is often perceived by laboratory staff as complex and quite distant from daily practice. Nevertheless, standards such as ISO/IEC 17025, ISO 15189 and ISO 17034 that specify requirements for laboratories to enable them to demonstrate they operate competently, and are able to generate valid results, require that measurement uncertainty is evaluated and reported. In response to this need, a European project entitled “Advancing measurement uncertainty—comprehensive examples for key international standards” started in July 2018 that aims at developing examples that contribute to a better understanding of what is required and aid in implementing such evaluations in calibration, testing and research. The principle applied in the project is “learning by example”. Past experience with guidance documents such as EA 4/02 and the Eurachem/CITAC guide on measurement uncertainty has shown that for practitioners it is often easier to rework and adapt an existing example than to try to develop something from scratch. This introductory paper describes how the Monte Carlo method of GUM (Guide to the expression of Uncertainty in Measurement) Supplement 1 can be implemented in R, an environment for mathematical and statistical computing. An implementation of the law of propagation of uncertainty is also presented in the same environment, taking advantage of the possibility of evaluating the partial derivatives numerically, so that these do not need to be derived by analytic differentiation. The implementations are shown for the computation of the molar mass of phenol from standard atomic masses and the well-known mass calibration example from EA 4/02.