Statistical method, and in particular, simulation is the most widely used operations research modelling approach now employed by industry and government. Validation of a simulation model is the procedure designed to determine whether the model is an accurate representation of the portion of reality under consideration. It is a process distinct from model building or debugging (although interrelated). In this work, strategies and practical numerical techniques for validating large-scale production simulation models (or any other large system) are detailed. An evolution of the central concepts of large-scale model validation is represented first. The distinction between model user's risk and model builder's risk is highlighted as an important development which alters the problem into a consideration of the basis a decision-maker should use in determining whether to rely on the outputs of such models. Two quantitative techniques which are applications of the Turing test and of mathematical programming are developed as recent advances within the traditional framework of this research area.