Pitfalls in uncertainty estimation : few measurements, non-gaussian distributions

In metrology practice, uncertainty budget calculation might include components based on little measurements data. The concept of coverage factor at a given level of confidence to combined uncertainty is taking care of this lack of information. We propose to apply a similar factor to the individual component(s) of an uncertainty budget when the value is calculated with limited number of measurements. These factors have the same function as the coverage factor defined in the GUM but on the level of the individual component of the total uncertainty. The goal of the work is to provide values of these factors for practical use. Inspired from the Supplement 1 of the GUM, we use Monte Carlo simulations to calculate the component coverage factors for repeated measurements from non-normal distributions. We apply the algorithm to the most common distributions in metrology, for 95% and 99% confidence level: rectangular, arc sine, triangular and trapezoidal distributions