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Demystifying EQA statistics and reports

机译:揭开EQA统计数据和报告的神秘面纱

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摘要

Reports act as an important feedback tool in External Quality Assessment (EQA). Their main role is to score laboratories for their performance in an EQA round. The most common scores that apply to quantitative data are Q- and Z-scores. To calculate these scores, EQA providers need to have an assigned value and standard deviation for the sample. Both assigned values and standard deviations can be derived chemically or statistically. When derived statistically, different anomalies against the normal distribution of the data have to be handled. Various procedures for evaluating laboratories are able to handle these anomalies. Formal tests and graphical representation techniques are discussed and suggestions are given to help choosing between the different evaluations techniques. In order to obtain reliable estimates for calculating performance scores, a satisfactory number of data is needed. There is no general agreement about the minimal number that is needed. A solution for very small numbers is proposed by changing the limits of evaluation.
Apart from analyte- and sample-specific laboratory evaluation, supplementary information can be obtained by combining results for different analytes and samples. Various techniques are overviewed. It is shown that combining results leads to supplementary information, not only for quantitative, but also for qualitative and semi-quantitative analytes.
机译:报告是外部质量评估(EQA)中重要的反馈工具。他们的主要作用是为实验室在EQA评估中的表现打分。适用于定量数据的最常见分数是Q和Z分数。要计算这些分数,EQA提供者需要为样本分配一个指定的值和标准偏差。分配值和标准偏差均可通过化学或统计学方法得出。从统计学上推导时,必须处理与数据的正态分布有关的不同异常。评估实验室的各种程序都能够处理这些异常情况。讨论了形式测试和图形表示技术,并提出了建议,以帮助您在不同的评估技术之间进行选择。为了获得用于计算性能分数的可靠估计,需要令人满意的数据数量。对于所需的最小数量没有普遍的共识。通过更改评估范围,提出了一种解决极少量问题的方法。
除了特定于分析物和样品的实验室评估之外,还可以通过组合不同分析物和样品的结果来获得补充信息。概述了各种技术。结果表明,结合结果不仅可以得到定量信息,还可以得到定性和半定量分析物的补充信息。

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