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142 Statistics in service to the nationand checking on robustness of conclusions to varying specifications. This experiencewas one among many that influenced how I continue to think aboutmy approach to Bayesian hierarchical modeling and its uses to the presentday; see, e.g., Airoldi et al. (2010).All too often academic statisticians think of their role as the productionof new theory and methods. Our motivation comes in large part from thetheoretical and methodological work of others. Our careers are built on, andjudged by, our publications in prestigious journals. And we often build ourcareers around such research and publishing activities.In this contribution, I want to focus on a different role that many statisticianshave and should play, and how this role interacts with our traditionalrole of developing new methods and publishing in quality journals. This isthe role we can fulfill in support of national activities and projects requiringstatistical insights and rigor. Much of my story is autobiographical, largelybecause I know best what has motivated my own efforts and research. Further,I interpret the words “to the nation” in my title quite liberally, so thatit includes election night forecasts and other public uses of statistical ideasand methods.In exploring this theme, I will be paying homage to two of the most importantinfluences on my view of statistics: Frederick Mosteller, my thesisadvisor at Harvard, and William Kruskal, my senior colleague at the Universityof Chicago, where I went after graduation; see Fienberg et al. (2007).One of the remarkable features of Mosteller’s autobiography (Mosteller,2010) is that it doesn’t begin with his early life. Instead, the volume beginsby providing chapter-length insider accounts of his work on six collaborative,interdisciplinary projects: evaluating the pre-election polls of 1948, statisticalaspects of the Kinsey report on sexual behavior in the human male, mathematicallearning theory, authorship of the disputed Federalist papers, safety ofanesthetics, and a wide-ranging examination of the Coleman report on equalityof educational opportunity. With the exception of mathematical learningtheory, all of these deal with important applications, where new theoryand methodology or adaption of standard statistical thinking was important.Mosteller not only worked on such applications but also thought it importantto carry the methodological ideas back from them into the mainstreamstatistical literature.A key theme I emphasize here is the importance of practical motivationof statistical theory and methodology, and the iteration between applicationand theory. Further, I want to encourage readers of this volume, especiallythe students and junior faculty, to get engaged in the kinds of problems I’lldescribe, both because I’m sure you will find them interesting and also becausethey may lead to your own professional development and advancement.