Though inherently statistical, model evaluation lacks a unifying statistical framework. NISS was hired to help find a overlying system to help with model evaluation. The research team used Bayesian techniques to measure the degree to which a model captures the underlying reality; theory and methods that allowed dual use of data in both estimation of model inputs and evaluation of outputs. SFCME also involved selection of evaluation functions by which a model and reality are compared. It also looked at design for determining what field or computer simulation data to collect.