Validation is the task that quantifies uncertainty in model results. Validation differs from calibration in that no parameters are adjusted. Model validation is needed for several reasons: (1) provides the user a valuable estimate of model uncertainty, (2) gives the modeling team, organization leadership, and the general public a measure of confidence, or skepticism, in the simulation results, (3) identifies bugs, problems, or limitations in the model that were previously unknown, and (4) illustrates what organizational data is needed for inventorying and monitoring the response variables in the future. The chapter presents a set of five levels of detail in which to conduct a validation and then enumerates the four steps of validation: (1) collect 134data, (2) format data, (3) run model, and (4) perform analyses. A set of analysis procedures are provided. Another form of validation is a sensitivity analysis, and details on conducting and interpreting a sensitivity analysis are also presented. Last, validation concerns are addressed.