Data inter-comparisons in the context of the knowledge-gaining process: an overview

Abstract

<p>This paper deals with the principle of data inter-comparisons, the object of which is to increase knowledge continuously with respect to time. Although the principle is as such nothing new to metrology and testing laboratories, which carry out experimental measurements, a degree of clarification is nonetheless called for in view of the numerous questions that arise concerning ways of implementing and utilizing it to improve knowledge capitalization.</p><p>The acquisition of knowledge relative to any measurand involves a series of steps: studying the state of knowledge of the measurand, choosing a working method (typically, by establishing a design of experiment), obtaining the measurements, and analyzing them. Following this, an action plan is established in order to reduce (or if possible avoid) weaknesses or over sensitivity.</p>Comparisons are already conducted using various approaches within a laboratory. It is therefore important to understand that to assess the accuracy of a method and validate it, it is necessary to compare the results obtained by several laboratories for a given method and measurand with the correct type of inter-comparison. It is this comparison between several laboratories that, when using different methods, produces the most up to date knowledge with the highest confidence level. This paper goes over the steps that allow developing knowledge, presenting the aims and characteristics of the various inter-laboratory comparison methods, notably referring to the tools established by documents such as the BIPM MRA (the Mutual Recognition Arrangement), the ISO 5725 and the ISO 13528.