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In this chapter we elucidate four main themes. The rst is that modern data analyses, including "Big Data" analyses, often rely on data from dierent sources, which can present challenges in constructing statistical models that can make eective use of all of the data. The second theme is that although data analysis is usually centralized, frequently the nal outcome is to provide information or allow decision-making for individuals. Third, data analyses often have multiple uses by design: the outcomes of the analysis are intended to be used by more than one person or group, for more than one purpose. Finally, issues of privacy and condentiality can cause problems in more subtle ways than are usually considered; we will illustrate this point by discussing a case in which there is substantial and eective political opposition to simply acknowledging the geographic distribution of a health hazard.
A researcher analyzes some data and learns something important. What happens next? What does it take for the results to make a dierence in people's lives? In this chapter we tell a story - a true story - about a statistical analysis that should have changed government policy, but didn't. The project was a research success that did not make its way into policy, and we think it
provides some useful insights into the interplay between locally-collected data, statistical analysis, and individual decision making.