This chapter brings together the argument so far, showing how nonignorable nonresponse may manifest itself and how the various models perform across these contexts, including how they may fail. It also highlights the ideal response to potential nonignorable nonresponse, which involves (1) creating randomized instruments, (2) using the randomized instrument to diagnose nonignorable nonresponse, (3) moving to conventional weights if there is no evidence of nonignorable nonresponse, but (4) using selection models explained here when there is evidence of nonignorable nonresponse. Section 11.1 simulates and analyzes data across a range of scenarios using multiple methods. Section 11.2 discusses how to diagnose whether nonresponse is nonignorable. Section 11.3 integrates the approaches with a decision tree based on properties of the data. Section 11.4 discusses how selection models can fail.