This is a brief conclusion arguing that the direction forward is clear, even if the path is not. The time for assuming away problems is past. We should begin with a paradigm that reflects all the ways that polling can go wrong and then identify, model, and measure all the sources of bias, not just the ones that are easy to fix. Much work remains to be done, though, as these new models and data sources will require much evaluation and development theoretically, empirically, and practically. The payoff will be that survey researchers will be able to remain true to their aspirations of using information about a small number of people to understand the realities about many people, even as it gets harder and hear from anyone, let alone the random samples that our previous theory relied on.

This chapter illustrates how to use randomized response treatments to assess possible nonresponse bias. It focuses on a 2019 survey and shows how nonignorable nonresponse may have deflated Trump support in the Midwest and among Democrats even as nonignorable nonresponse inflated Trump support among Republicans. We also show that Democrats who responded to the poll were much more liberal on race than Democrats who did not respond, a pattern that was particularly strong among White Democrats and absent among non-White Democrats. Section 12.1 describes a survey design with a randomized response instrument. Section 12.2 discusses nonignorable nonresponse bias for turnout questions. Section 12.3 looks at presidential support, revealing regional and partisan differences in nonignorable nonresponse. Section 12.4 looks at race, focusing on partisan and racial differences in nonignorable nonresponse. Section 12.5 assesses nonignorable nonresponse on climate, taxes, and tariffs.