These case studies show work on applying the guide to the expression of uncertainties in measurement (GUM) and work on uncertainties in drugs testing in sport.

Up a GUM tree? Try the Full Monte! ( PDF 167 KB)

Sampling techniques, such as Monte Carlo simulation, provide an alternative approach to uncertainty evaluation in which the propagation of uncertainties is undertaken numerically rather than analytically. Such techniques are useful for validating the results returned by the application of the GUM, as well as in circumstances where the assumptions made by the GUM do not apply. In fact, these techniques are able to provide much richer information, by propagating the distributions (rather than just the uncertainties) of the input quantities through the measurement model, to provide the distribution of the output quantity. From the output distribution confidence intervals or confidence regions (in the multivariate case) can be produced, as can other statistical information.