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In the twenty-first century, we are able to build large, complex statistical models that are very much like the scientific processes they represent. We use diagnostics to highlight inadequacies in the statistical model, and because of the complexity many different diagnostics are needed. This is analogous to the process of diagnosis in the medical field, where a suite of diagnostics is used to assess the health of a patient.

This chapter is focussed on evaluating model diagnostics. In the medical literature a structured approach to diagnostic evaluation is used, based on measurable outcomes such as Sensitivity, Specificity, ROC curves, and False Discovery Rate. We suggest using the same framework to evaluate model diagnostics for hierarchical spatial statistical models; we note that the concepts are the same in the non-spatial and non-hierarchical setting, although the specific proposals given in this chapter may be difficult to generalize.