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The S-score is a new performance score applicable to proficiency testing (PT) for binary data. It allows the proficiency test provider to choose how the assigned value (AV) is defined, e.g., positive versus negative, either by the PT provider or by laboratory consensus based on participating laboratories. It is a flexible tool that can be used to select as a maximum four types of proficiency test items (PTIs) depending on the required correct results and expected correct results predefined by the PT provider. Regarding the required correct results, for an easy tasks, e.g., easy analyte detection, correct results matching with the assigned value are required for all PTI1s. For difficult tasks, e.g. close to the limit of detection (LOD) with replicated samples and for mixed tasks with unreplicated, an acceptable range of correct results is required using the binomial probability density from at least six PTI2s. Regarding correct results expected but not required for difficult tasks, results matching with the assigned value are requested by the PT provider for all PTI3s and by a participating laboratory consensus (LC) for all PTI4s. The S-score was designed as a numerical indicator taking into account all the combinations of the different types of PTIs. The decimal part summarizes the laboratory results indicating the rate of incorrect results in relation to the different PTIs assigned values. The integer part provides a similar interpretation to the one of the z-score for quantitative PT, with 'satisfactory' (1 ≤ S-score < 2), 'questionable' (2 ≤ S-score < 3), and 'unsatisfactory' (S-score ≥ 3) performance, respectively. [ABSTRACT FROM AUTHOR]

In May, 2023, the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) published the international standard ISO/IEC 17043:2023 Conformity assessment—General requirements for the competence of proficiency testing providers. This article describes the 100 % online revision process from the perspective of the Working Group (WG) members, explains the latest changes, and explores the potential impact of these changes on interested parties. [ABSTRACT FROM AUTHOR]

Data processing of microbial enumeration expressed as colony counts requires the use of specific statistical approaches due to the particular aspect of the analyte and the consideration of the variability related to the growth of microorganisms. A challenging matter in the organization of proficiency testing (PT) schemes for water microbiology is to provide representative, homogeneous and stable enough samples with the aim of assessing participants' performance but also characterizing the accuracy of measurement. As a consequence, the proficiency testing design may help to make clear distinction between the different sources of variation and facilitate the subsequent error analysis associated with the analytical procedures of the participants. Besides, the statistical tools may be selected to provide explicit outcomes which enable the participants to interpret the data in line with other existing indicators such as those arising from validation studies or measurement uncertainty procedures in the laboratory quality assurance system. In this paper, the suitability of a Poisson–Gamma hierarchical generalized linear model is tested in order to evaluate the interlaboratory error, the batch homogeneity and the repeatability error from water microbiology PT. A probabilistic approach deriving from the negative binomial distribution is proposed for assessing the participating laboratories performance in terms of generalized z-score. [ABSTRACT FROM AUTHOR]