We strive to specify models that resemble data collected in studies or observed from processes. One way to check whether the model is a reasonable abstraction of reality is to display the data in the model space, such as residual plots for linear models. While these plots are well-behaved for simple models, such as linear regression with uncorrelated errors, this is not the case for more-complex models. For example, residual plots for multilevel models often show patterns that are artifacts of the model-fitting process, and are not indicative of a model deficiency. This talk will outline how visual inference can be utilized during model validation for multilevel models, and how this approach can be generalized to other models. I will also discuss how these techniques have informed how I teach model validation to undergraduate students.