Statistical evaluation of interlaboratory tests

Davies, Paul

doi:10.1007/bf00467041

Cited by 56 publications

(24 citation statements)

References 4 publications

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“…Even more, the detection of outliers has various flaws, like masking and swamping. Masking means that an outlier is not detected by the presence of another outlier, swamping means that a non-outlying observation is falsely indicated as an outlier ( 3 , 21 , 22 ). …”

Section: Outlier Removalmentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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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.

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Section: Outlier Removalmentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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“…Evaluation of results was based on theoretical reference concentrations rather than laboratory average results. Identification of outliers was carried out by the Hampel test, which is recommended for statistical evaluation of interlaboratory comparison studies by Davies [22]. Subsequent to the elimination of outliers, the participants' results were expressed as percent recovery of theoretical MTBE reference concentrations.…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

Interlaboratory comparison study for the determination of methyl tert -butyl ether in water

Schuhmacher¹,

Führer²,

Kandler³

et al. 2003

Analytical and Bioanalytical Chemistry

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This is the first publication which describes the evaluation of the analytical performance and state-of-the-art of the determination of methyl tert-butyl ether (MTBE) in water at ng L(-1) concentrations. An interlaboratory comparison study for the determination of MTBE in water was carried out. Twenty-eight laboratories from seven European countries participated in the study. Twenty of those finally transmitted results to the organiser. Italian spring water, containing no detectable amounts of MTBE was fortified to yield two samples with MTBE concentrations of 0.074 +/- 0.004 microg L(-1) and 0.256 +/- 0.010 microg L(-1). The laboratories applied their regular in-house methods to analyse the water samples. Static headspace, Purge & Trap, solid-phase microextraction (SPME) or direct aqueous injection were used as sample preparation techniques. Subsequent separation and detection of MTBE were performed by gas chromatography/mass spectrometry (GC/MS) or gas chromatography/flame ionisation detection (GC/FID). After rejection of outliers, the overall arithmetic mean of laboratory results corresponded to recoveries of 78 +/- 20% (Sample A) and 88 +/- 20% (Sample B) of the reference concentrations. The between laboratory coefficients of variation (CV) were 32% and 31%, respectively. The organisation of the study and quality assurance measures at the organiser's laboratory are described. Moreover, the measurement results of the participants and the analytical methods used for the determination of MTBE are presented and the correlation between selected method parameters and data quality is discussed.

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“…Three additional outlier tests were also applied to the data. The robust MAD (median absolute deviation from the median)-based test of Davies [24] and Linsinger et al [26], at 95% confidence, identified one outlier (59.5 ng g À1 ). The robust Z-modified approach using the normalized interquartile range (IQR), with Z j j > 3 [34] flagged the same outliers as the box plot.…”

Section: An Examination Of Pt Concentrations In Bcr-723mentioning

confidence: 98%

“…Another advantage of robust procedures is that they are designed to downplay outliers, and assume that contamination is a likely outcome of analytical measurement. Robust central tendency and location estimation, graphical exploratory data analysis (EDA), robust outlier examination, and bootstrapping are now widely accepted in environmental studies [22], in proficiency testing in analytical chemistry laboratories, and in interlaboratory data evaluations [23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A re-examination of platinum-group element concentrations in the environmental certified reference material BCR-723

Sutherland

2007

International Journal of Environmental Analytical Chemistry

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Statistical evaluation of interlaboratory tests

Cited by 56 publications

References 4 publications

Demystifying EQA statistics and reports

Demystifying EQA statistics and reports

Interlaboratory comparison study for the determination of methyl tert -butyl ether in water

A re-examination of platinum-group element concentrations in the environmental certified reference material BCR-723

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