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.

实验室工作人员通常认为测量不确定度的评估很复杂，而且与日常实践相去甚远。尽管如此，ISO/IEC 17025、ISO 15189 和 ISO 17034 等标准规定了实验室的要求，使他们能够证明他们能够胜任运作，并能够产生有效的结果，要求对测量不确定度进行评估和报告。为满足这一需求，2018 年 7 月启动了一个名为“推进测量不确定度——关键国际标准的综合示例”的欧洲项目，旨在开发有助于更好地理解所需内容并帮助实施此类校准评估的示例，测试和研究。该项目采用的原则是“以身作则”。EA 4/02 和 Eurachem/CITAC 测量不确定度指南等指导文件的过往经验表明，对于从业者而言，返工和调整现有示例通常比尝试从头开发更容易。这篇介绍性论文描述了 GUM（测量不确定性表达指南）增补 1 的蒙特卡罗方法如何在 R 中实现，R 是一种数学和统计计算环境。不确定性传播定律的实现也在相同的环境中呈现，利用数值计算偏导数的可能性，因此这些不需要通过解析微分来推导。