Metrology Training Course

Evaluation of measurement uncertainty - GUM and Monte-Carlo methods

Context and purpose of the training

A calibration, test or analysis result must be accompanied by its uncertainty. This is a normative requirement, it can also be an opportunity to take a step back to reflect on the measurement process in order to better control it by identifying and evaluating the different sources of uncertainty.

This training is designed in a structured and progressive way to assess measurement uncertainties according to the reference approach (GUM) and the extension to the Monte-Carlo method.

Goals

  • Understand the interest of measurement uncertainty.
  • Assimilate the basic vocabulary and concepts for the evaluation of uncertainties.
  • Know how to evaluate the uncertainty associated with a measurement result according to the GUM method.
  • Know and apply the principle of the Monte Carlo method.
  • Have the necessary tools for assessing uncertainties, based on the most recent texts.

Program

  • Introduction
  • Measurement uncertainties - Why?
  • Some definitions
  • Reminder of statistical notions
  • Errors, effects and corrections
  • Accuracy/bias, fidelity and exactness
  • The GUM methodology
  • Expression of the result and Rounding rule
  • Examples of uncertainty balance sheet or uncertainty budget
  • Examples of calibration certificate
  • Monte Carlo method - Supplement 1 of the GUM
  • Conclusion

Pedagogical method

Conventional presentation. Exchange and discussion. Presentation of examples from the standards.
Exercises (stated and corrected).

Target audience

Staff of test or calibration laboratories: metrology manager, technical manager, test engineers, quality manager, technicians, etc.

Necessary tools

Laptop with Excel spreadsheet (or equivalent).

Duration

Two days (a detailed program can be sent on request).

Trainer

Pr. Christophe Ley - Professor of Mathematics & Applied Statistics - University of Luxembourg

In partnership with the University of Luxembourg

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