Performance comparison between expanded uncertainty evaluation algorithms

Abstract

The use of normal approximation to estimate expanded uncertainty has been very widespread; yet this is one of the practices that is being criticized by various quarters for lack of rigor and potentially misleading. Monte Carlo method is probably the only method trusted to generate reliable expanded uncertainty. Unfortunately, Monte Carlo method is not applicable for type-A evaluations. This is one of the challenges faced by current researchers in measurement community. This paper presents the comparison of expanded uncertainty estimation accuracy between Monte Carlo method, normal approximation and four well-known moment based distribution fitting methods. The Cornish-Fisher approximation is found to be consistently better than normal approximation but none of the moment based approach is comparable to Monte Carlo method in terms of accuracy and consistency.