Data comparisons and uncertainty: a roadmap for gaining in competence and improving the reliability of results

Abstract

This paper traces a roadmap for gaining in competence and for improving the reliability of results in a laboratory. The roadmap was built from the requirements concerning the results quality and measurement uncertainty, which accreditation bodies use for the accreditation of testing and calibration laboratories. In industry, accreditation is the accepted proof of a laboratory's assigned level of competence. The level of performance of a laboratory is demonstrated through the quality of its management of test and calibration results. Inter-laboratory comparisons and the evaluation of measurement uncertainties are recommended as the most appropriate methods for demonstrating continuous improvement in laboratories. The common methods used for data comparisons and for the evaluation of measurement uncertainties are highlighted. An overview of the main indicators used in data comparisons is presented. Some recommendations are made that are useful to the design of a roadmap for gaining in competence and for improving the quality of results obtained by a laboratory.