Evaluation and Analysis of Measurement Uncertainty

Abstract

Reliability of measurement data is particularly important in today’s world of globalization and trading across international borders. The acceptable and meaningful way of expressing the measurement result is to give the average value along with a range called measurement uncertainty at a confidence level within which the true value shall lie. Measurement uncertainty has specifically gained more importance with emphasis on decision rule as the decision-making criteria in ISO 17025: 2017 for accepting or rejecting a given sample. It is important for laboratory personnel to have knowledge in statistics apart from expertise in their field of testing and calibration to be able to correctly determine the factors that would contribute to the measurement uncertainty and the values of such contributions thereof. This chapter provides an overview of the evaluation and analysis of measurement uncertainty. The study also describes the strengths, weaknesses, opportunities, and threats (SWOT) analysis of the Monte Carlo simulation (MCS) in the evaluation of measurement uncertainty. Furthermore, the chapter also summarizes the implications and future prospects associated with the uncertainty methodology and recommends the wide usage of this in solving problems in various scientific filed.KeywordsMetrologyMeasurement uncertaintyProbability distribution functionSWOTCalibration