The formulation of a comprehensive process to validate computational models depends on a number of factors, including how the models are to be used, the nature of the models and the characteristics of the available data. However, in all cases determining whether the model and the available data are consistent in light of the uncertainties in both is fundamental. In this lecture, after a brief overview of the challenges of model validation, we will discuss techniques for assessing consistency between uncertain models and uncertain data. Included will be discussion of probabilistic validation criteria, the consequences of epistemic and aleatoric uncertainties, the design and use of statistical test quantities, and the selection of data for validation.