Online Course of Measurement Uncertainty Estimation in Analytical Chemistry

We are glad to introduce the Online Course of Measurement Uncertainty Estimation in Analytical Chemistry!

It has been set up at University of Tartu during 2013 and its version 1 is now accessible from the address
http://sisu.ut.ee/measurement/. This is an introductory course on estimation of measurement uncertainty, specifically related to chemical analysis. The course gives the main concepts and mathematical apparatus of measurement uncertainty estimation and explains in detail two principal approaches to measurement uncertainty estimation – the ISO GUM modeling approach (the “bottom-up” approach) and the single-lab validation approach as implemented by Nordtest (the “top-down”) approach. The course contains close to 50 short video lectures, practical examples and numerous tests and exercises for self-testing.

In spite of being introductory, the course intends to offer sufficient knowledge and skills for carrying out uncertainty estimation for the common analyses in routine laboratory environment. The examples or exercises currently include measurement uncertainty in acid-base titration, measurement uncertainty in Kjeldahl nitrogen determination, measurement uncertainty in UV-Vis spectrophotometry, measurement uncertainty in atomic absorption spectroscopy and measurement uncertainty in liquid chromatography mass spectrometry (LC-MS) and more is coming. It is important to stress, however, that for successful measurement uncertainty estimation experience (both in analytical chemistry as such and also in uncertainty estimation) is crucial and this can be acquired only through practice.

This course can be used via web by anyone who wishes to improve the knowledge and skills in measurement uncertainty estimation. It will also be offered as a registered (i.e. giving credit points) free online course (interfaced with the Moodle study environment) starting from Spring semester of 2014 as to students from all over the world.

Needless to say, all feedback is most welcome!

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