This section of the book presents an in-depth discussion of the topic of model validation and prediction. As discussed in Part I, this book uses the restricted meaning of model validation, i.e., assessment of model accuracy as determined by comparison of model outputs with experimental measurements. Stated differently, if you are not comparing model predictions of system response quantities (SRQs) with experimental measurements of the SRQs for the purpose of assessing the accuracy of the model, you are not conducting model validation. Prediction, as discussed in Part I, deals with the use of the model and all information that is available concerning the system of interest, as well has how well the model has performed in model validation activities, to predict the response of the system of interest for which no experimental data is presently available. That is, we use the model to foretell the response of the system of interest given our knowledge of the system, and how well the model has compared with available experimental measurements, including our estimates of all of the uncertainties involved in every element of the simulation.