During the past decade with increased computing power and new research developments, Bayesian statistical methods have become practical in diverse areas of statistical applications. Bayesian methods provide a unified framework for optimally combining information from multiple sources, resulting in simpler and improved statistical analyses. Despite the widespread growth in Bayesian methods, for the most the field of metrology has not taken advantage of these methods.
From a computational perspective, Bayesian methods can be viewed as a natural extension of familiar confidence intervals and significance tests, which sheds light on their meaning. This viewpoint shows that no special software is required to compute Bayesian results, leaving the distinctions between conventional and Bayesian analyses in the conceptual realm. Key Bayesian concepts may be grasped more easily than those required for proper use of conventional methods. These concepts allow one to re-examine results from a Bayesian perspective, to complement and prevent misinterpretation of conventional results.