The Three Most Common Needs for Training on Measurement Uncertainty
DOI:
https://doi.org/10.2478/msr-2025-0029Keywords:
measurement uncertainty, Education, trainingAbstract
Measurement uncertainty is key to assessing, stating and improving the reliability of measurements. An understanding of measurement uncertainty is the basis for confidence in measurements and is required by many communities; among others in national metrology institutes, accreditation bodies, calibration and testing laboratories, as well as in legal metrology, at universities and in different metrology fields. An important cornerstone to convey an understanding of measurement uncertainty is to provide training.
This article identifies the status and the needs for training on measurement uncertainty in each of the above communities as well as among those teaching uncertainty. It is the first study to do so across many different disciplines, and it merges many different sources of information with a focus on Europe. As a result, awareness on the training needs of different communities is raised and teachers of uncertainty are supported in addressing their audiences’ needs as well as in improving their uncertainty-specific pedagogical and technology-related knowledge.
The three needs that are most commonly encountered in the communities requiring an understanding of measurement uncertainty, are 1) to address a general lack of training on measurement uncertainty, 2) to gain a better overview of existing training on measurement uncertainty in several communities, and 3) to deliver more training on specific technical topics including use of a Monte Carlo method for propagating probability distributions and treating multivariate measurands and measurement models. These needs will serve to guide future developments in uncertainty training and will, ultimately, contribute to increasing the understanding of uncertainty.
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 Slovak Academy of Sciences - Institute of Measurement Science

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.