Key comparison data are often reported, as a matter of policy, with respect to a reference value calculated from the pool of the participants' results. Consideration of the comparison data sets, particularly with regard to the consequences and implications of such data pooling, can justify the choice of a simple statistical reference value.Recent key comparisons in thermometry are examined to illustrate the nature of the problem, and the utility of a simple approach to creating pooled data distributions. An objective criterion is introduced to help determine when a comparison can safely be described as a single population having a key comparison reference value (KCRV). Graphical techniques of data pooling are presented as an aid to harnessing expert opinion on this question.
Although numerous investigations have been conducted in an attempt to identify the optimal corticosteroid agent, and its optimal dosing regimen for the intra-articular treatment of osteoarthritis, a consensus has not been established. The current recommendations for dosing interval appear to have arisen as a consequence of a misinterpretation of previously published works. This paper recommends that practitioners refine and individually tailor their selection of agent and dosing regimen to patient needs and clinical response.
Pair-difference chi-squared statistics are useful for analysing metrological consistency within a Key Comparison. We show how they relate to classical chi-squareds and how they can be used with full rigour, for any comparison of a scalar measurand, to compare the observed dispersion of results with the dispersion that would be expected on the basis of the claimed uncertainties. In several limits, the distributions of these pair-difference statistics are exact chi-squareds. For other cases, the Monte Carlo method can evaluate the distributions even in the presence of non-Gaussian uncertainty distributions, including the Student distribution to be construed when a participant has reported a degrees of freedom. Monte Carlo methods also treat inter-laboratory covariances in a transparent manner appropriate for metrology. Pair-difference chi-squared statistics are independent of the choice of a Key Comparison Reference Value (KCRV) and so may expedite the process of analysis, consensus building and publication for Key Comparisons. They are appropriate for judging pair metrology. We discuss them as a necessary, but not sufficient, test for a Key Comparison reported in the conventional way using a KCRV. The deficiencies of using solely the classical chi-squared test are discussed. A good remedy is available with pair-difference chi-squared statistics.
We examine chi-squared statistics that are appropriate for analysing the adequacy of different key comparison reference value (KCRV) candidates in accounting for the observed dispersion of results of a key comparison, about the candidate estimator and within the stated uncertainty claims. We extend the analysis to cover cases where the uncertainty budgets incorporate low degrees of freedom or have significant correlations. In this context, we discuss when it is important to view the KCRV as a method and not merely as a number. To use these statistics for the usual chi-squared tests of consistency, the required distributions (that can depart from the exact chi-squared distribution) can readily be evaluated by Monte Carlo simulation, for any KCRV algorithm that uses only the peer results of the comparison. Similarly, the effects of non-Gaussian distributions (such as the Student distribution implied when a participant has reported finite degrees of freedom) can be evaluated with the requisite precision.
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