MEASUREMENT UNCERTAINTY: CONCEPTUAL FOUNDATIONS AND PRACTICAL IMPLICATIONS

A basic idea in all quantitative sciences and engineering fields, measurement uncertainty gives a numerical estimate of the quality of a measurement result. Measurement results cannot be compared without it either among themselves or in line with reference values specified in a specification or standard. Mostly driven by the "Guide to the Expression of Uncertainty in Measurement," this paper explores the conceptual roots of measurement uncertainty, follows its historical development, and investigates the main definitions and terminologies that support its modern understanding. We investigate the mathematical and statistical models for Type A and Type B evaluation approaches, the law of propagation of uncertainty, and the computation of enlarged uncertainty. Moreover, the pragmatic consequences of measurement uncertainty are examined closely in many different fields, including scientific research, regulatory compliance, quality control, calibration, and conformity assessment. Advanced subjects, including the function of correlation, Bayesian methods, and Monte Carlo techniques in uncertainty evaluation, are also covered in this paper. Last but not least, current difficulties and future directions in the field of measurement uncertainty, especially in relation to complicated systems, big data, and artificial intelligence, are discussed, so stressing the continuous need of strong and harmonious solutions. Keywords: measurement uncertainty, metrology, gum, error analysis, calibration, conformity assessment, risk management.