Concepts, Methods, and Tools Enabling Measurement Quality

This contribution provides an overview, illustrated with examples, of applications of statistical methods that support measurement quality and guarantee the intercomparability of measurements made worldwide, in all fields of commerce, industry, science, and technology, including medicine. These methods enable a rigorous definition of measurement uncertainty, and provide the means to evaluate it quantitatively, both for qualitative measurands (for example, the sequence of nucleobases in a DNA strand) and for quantitative measurands (for example, the mass fraction of arsenic in rice). Measurement quality is its trustworthiness and comprises several attributes: reliable calibration involving standards; traceability to the international system of units or to other generally recognized standards; measurement uncertainty that realistically captures contributions from all significant sources of uncertainty; and fitness for purpose of the measurement results, which comprise measured values and evaluations of associated uncertainties. Statistical methods play key roles in the quality system that validates the measurement services (reference materials, calibrations, reference data, and reference instruments) provided by the National Institute of Standards and Technology (NIST). And these services in turn support measurement quality in laboratories, factories, farms, hospitals, transportation, utilities, and weather and environmental monitoring stations throughout the world, contributing to ensure food safety, to manufacture reliable products, and to monitor industrial and natural processes accurately. The NIST Uncertainty Machine (NUM) and the NIST Consensus Builder (NICOB) are web-based tools freely available to metrologists everywhere, that help maintain measurement quality. The NUM serves to evaluate measurement uncertainty and the NICOB builds consensus values from measurement results obtained independently for the same measurand.