Explanatory power of degrees of equivalence in the presence of a random instability of the common measurand

Wübbeler, Gerd; Bodnar, Olha; Mickan, Bodo; Elster, Clemens

doi:10.1088/0026-1394/52/2/400

Cited by 11 publications

(12 citation statements)

References 15 publications

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“…The power denotes the probability, prior to carrying out the key comparison, that an underrated uncertainty will be detected through checking the degree of equivalence. Analytical expressions for the power as well as for the loss of power due to instability of the transfer standard are given in [20] and can be used to analyze the scenarios shown in Figure 6. The power results in this work were determined by assuming that the Type A contribution was negligible.…”

Section: Explanatory Powermentioning

confidence: 99%

“…For selected threshold values, Criteria B and C will give the same results: Criterion C based on the maximal explanatory power resembles the behavior of the heuristic Criterion B. But note that when specifying a maximum tolerable power loss L th the corresponding upper limit for the uncertainty ratio u TS / u base i depends on u TS , on u base i and on all uncertainties u base j , j ≠ i quoted by the remaining laboratories [20]. …”

Section: Criteria Applied To Bi-lateral Comparison Example and Promentioning

confidence: 99%

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Transfer standard uncertainty can cause inconclusive inter-laboratory comparisons

Wright¹,

Toman²,

Mickan³

et al. 2016

Metrologia

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Inter-laboratory comparisons use the best available transfer standards to check the participants’ uncertainty analyses, identify underestimated uncertainty claims or unknown measurement biases, and improve the global measurement system. For some measurands, instability of the transfer standard can lead to an inconclusive comparison result. If the transfer standard uncertainty is large relative to a participating laboratory’s uncertainty, the commonly used standardized degree of equivalence ≤ 1 criterion does not always correctly assess whether a participant is working within their uncertainty claims. We show comparison results that demonstrate this issue and propose several criteria for assessing a comparison result as passing, failing, or inconclusive. We investigate the behavior of the standardized degree of equivalence and alternative comparison measures for a range of values of the transfer standard uncertainty relative to the individual laboratory uncertainty values. The proposed alternative criteria successfully discerned between passing, failing, and inconclusive comparison results for the cases we examined.

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Section: Explanatory Powermentioning

confidence: 99%

Section: Criteria Applied To Bi-lateral Comparison Example and Promentioning

confidence: 99%

Transfer standard uncertainty can cause inconclusive inter-laboratory comparisons

Wright¹,

Toman²,

Mickan³

et al. 2016

Metrologia

Self Cite

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“…In [14], several studies on the stability of gas mixtures are discussed and, in addition to model choices that account for instability, a fixed or random effects model is considered (see also [13,15]). For a discussion on random stability effect models, we refer to [16,17]. Also [18] discusses deterministic and random drift models that introduce time-dependency of the travelling standard.…”

Section: Introductionmentioning

confidence: 99%

On modelling of artefact instability in interlaboratory comparisons

et al. 2023

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Key comparisons are at the core of metrology and support the international equivalence of measurementstandards. Typical key comparison setups involve travelling artefacts which are transferred between theparticipants of a study. After each participating laboratory performs its measurements of the artefacts, asubsequent analysis reveals the degree of equivalence between the participants. For this analysis stage, thestability of the artefacts plays a crucial role, and violations of the stability need to be taken into account toallow for a meaningful comparison. In this work, we present several mathematical models for a treatment ofnon-negligible artefact instability effects in bilateral comparisons. We highlight the underlying model assumptionsand derive analytical formulae for the estimate and standard uncertainty of the instability effect. Moreover, wederive the bilateral degree of equivalence by applying the models in a treatment essentially based on the GUM(JCGM-100). Our considerations conclude with numerical experiments using data from a bilateral comparisonon illuminance and from a recent CCM key comparison of kilogram realisations.

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“…This proliferation of options demonstrates two things: first, that a single analysis method is unlikely to be suitable for all comparisons; and second, that there are few tools available to compare one method with another in any given comparison scenario. In particular, apart from one study that looked at artefact drift during a comparison [12], little attention has been paid to the statistical power of various methods.…”

Section: Introductionmentioning

confidence: 99%

The Statistical Power and Confidence of Some Key Comparison Analysis Methods to Correctly Identify Participant Bias

et al. 2021

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The validity of calibration and measurement capability (CMC) claims by national metrology institutes is supported by the results of international measurement comparisons. Many methods of comparison analysis are described in the literature and some have been recommended by CIPM Consultative Committees. However, the power of various methods to correctly identify biased results is not well understood. In this work, the statistical power and confidence of some methods of interest to the CIPM Consultative Committees were assessed using synthetic data sets with known properties. Our results show that the common mean model with largest consistent subset delivers the highest statistical power under conditions likely to prevail in mature technical fields, where most participants are in agreement and CMC claims can reasonably be supported by the results of the comparison. Our approach to testing methods is easily applicable to other comparison scenarios or analysis methods and will help the metrology community to choose appropriate analysis methods for comparisons in mature technical fields.

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Explanatory power of degrees of equivalence in the presence of a random instability of the common measurand

Cited by 11 publications

References 15 publications

Transfer standard uncertainty can cause inconclusive inter-laboratory comparisons

Transfer standard uncertainty can cause inconclusive inter-laboratory comparisons

On modelling of artefact instability in interlaboratory comparisons

The Statistical Power and Confidence of Some Key Comparison Analysis Methods to Correctly Identify Participant Bias

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