Possolo Amanda, Koepke Amanda, Newton David, Winchester Michael R
National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
National Institute of Standards and Technology, Boulder, CO 80305, USA.
J Res Natl Inst Stand Technol. 2021 Apr 27;126:126007. doi: 10.6028/jres.126.007. eCollection 2021.
This contribution describes a Decision Tree intended to guide the selection of statistical models and data reduction procedures in key comparisons (KCs). The Decision Tree addresses a specific need of the Inorganic Analysis Working Group (IAWG) of the Consultative Committee (CC) for Amount of Substance, Metrology in Chemistry and Biology (CCQM), of the International Committee for Weights and Measures (CIPM), and it is likely to address similar needs of other working groups and consultative committees. Because the portfolio of KCs previously organized by the CCQM-IAWG affords a full range of opportunities to demonstrate the capabilities of the Decision Tree, the majority of the illustrative examples of application of the Decision Tree are from this working group. However, the Decision Tree is widely applicable in other areas of metrology, as illustrated in examples of application to measurements of radionuclides and of the efficiency of a thermistor power sensor. The Decision Tree is intended for use after choices will have been made about the measurement results that qualify for inclusion in the calculation of the key comparison reference value (KCRV), and about the measurement results for which degrees of equivalence should be produced. Both these choices should be based on substantive considerations, not on purely statistical criteria. However, the Decision Tree does not require that the measurement results selected for either purpose be mutually consistent. The Decision Tree should be used as a guide, not as the sole and autonomous determinant of the model that should be selected for the measurement results obtained in a KC, or of the procedure that should be employed to reduce these results. The scientists running the KCs ultimately have the freedom and responsibility to make the corresponding choices that they deem most appropriate and that best fit the purpose of each KC. The Decision Tree involves three statistical tests, and comprises five terminal leaves, which correspond to as many alternative ways in which the KCRV, its associated uncertainty, and the degrees of equivalence (DoEs) may be computed. This contribution does not purport to suggest that any of the KCRVs, associated uncertainties, or DoEs, presented in previously approved final reports issued by working groups of the CCs should be modified. Neither do the alternative results question existing, demonstrated calibration and measurement capabilities (CMCs), nor do they support any new CMCs.
本论文介绍了一种决策树,旨在指导关键比对(KC)中统计模型的选择和数据精简程序。该决策树满足了国际计量委员会(CIPM)化学与生物计量学物质的量咨询委员会(CC)下属无机分析工作组(IAWG)的特定需求,并且可能满足其他工作组和咨询委员会的类似需求。由于CCQM - IAWG之前组织的KC组合提供了全方位展示决策树功能的机会,因此决策树应用的大多数示例均来自该工作组。然而,决策树在计量学的其他领域也广泛适用,如放射性核素测量以及热敏电阻功率传感器效率测量的应用示例所示。决策树旨在用于对符合纳入关键比对参考值(KCRV)计算的测量结果以及应给出等效度的测量结果做出选择之后。这两种选择都应基于实质性考虑,而非纯粹的统计标准。然而,决策树并不要求为任一目的所选的测量结果相互一致。决策树应作为一种指导,而非在KC中获得的测量结果应选用的模型或应采用的精简这些结果的程序的唯一自主决定因素。负责KC的科学家最终有权且有责任做出他们认为最合适且最符合每个KC目的的相应选择。决策树涉及三项统计测试,并包含五个终端叶节点,它们对应于计算KCRV、其相关不确定度以及等效度(DoE) 的五种不同方式。本论文无意建议修改CC各工作组先前批准的最终报告中给出的任何KCRV、相关不确定度或DoE。替代结果也未对现有的、已证明的校准和测量能力(CMC)提出质疑,也不支持任何新的CMC。