Determining the degree of equivalence of participating laboratories from the results of measurement comparisons still prompts discussions among metrologists, especially where measurement uncertainties have been underestimated. This paper expands on a solution to a problem issued in 2020 by the Journal of Analytical and Bioanalytical Chemistry. The example illustrates an approach to consensus-building based on Bayesian selection among statistical models that attempt to explain the excess of data variation. The probability of any further model being correct can be similarly calculated.

中文翻译：

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实验室间共识建立

根据测量比较结果确定参与实验室的等效程度仍然会引起计量学家之间的讨论，尤其是在测量不确定度被低估的情况下。本文扩展了分析和生物分析化学杂志2020 年发布的一个问题的解决方案。该示例说明了一种基于贝叶斯选择的建立共识的方法，这些模型试图解释过度的数据变化。可以类似地计算任何进一步模型正确的概率。