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R.J. Carroll 57747.8 After the Presidents’ AwardSince the COPSS Award, my main interests have migrated to problems in epidemiologyand statistical methods to solve those problems. The methods includedeconvolution, semiparametric regression, measurement error, and functionaldata analysis, which have touched on problems in nutritional epidemiology,genetic epidemiology, and radiation epidemiology. I have even becomea committed Bayesian in a fair amount of my applied work (Carroll, 2013).Ihavefoundproblemsinnutritionalepidemiologyparticularlyfascinating,because we “know” from animal studies that nutrition is important incancer, but finding these links in human longitudinal studies has proven tobe surprisingly difficult. I remember an exquisite experiment done by JoanneLupton (now a member of the US Institute of Medicine) and Nancy Turnerwhere they fed animals a diet rich in corn oil (the American potato chip diet)versus a diet rich in fish oil, exposed them to a carcinogen, and within 12hours after exposure all the biomarkers (damage, repair, apoptosis, etc.) litup as different between the two diets, with corn oil always on the losing end.When the microarray became the gold standard, in retrospect a sad and veryfunny statement, they found that without doing anything to the animals, 10%of the genes were different at a false discovery rate of 5%. Diet matters!There are non-statisticians such as Ed Dougherty who think the field ofstatistics lost its way when the microarray came in and thinking about hypotheses/epistemologywent out (Dougherty, 2008; Dougherty and Bittner,2011): “Does anyone really believe that data mining could produce the generaltheory of relativity?” I recently had a discussion with a very distinguishedcomputer scientist who said, in effect, that it is great that there are manycomputer scientists who understand (Bayesian) statistics, but would it not begreat if they understood what they are doing scientifically? It will be veryinteresting to see how this plays out. Statistical reasoning, as opposed to computation,while not the total domain of statisticians, seems to me to remaincrucial. To quote from Dougherty and Bittner (2011),“The lure of contemporary high-throughput technologies is that theycan measure tens, or even hundreds, of thousands of variables simultaneously,thereby spurring the hope that complex patterns of interactioncan be sifted from the data; however, two limiting problems immediatelyarise. First, the vast number of variables implies the existenceof an exponentially greater number of possible patterns in the data,the majority of which likely have nothing to do with the problem athand and a host of which arise spuriously on account of variation inthe measurements, where even slight variation can be disastrous owingto the number of variables being considered. A second problem is thatthe mind cannot conceptualize the vast number of variables. Soundexperimental design constrains the number of variables to facilitate