As academic statisticians, we are missing the boat. We are barking up the wrong tree. We do not see what is plainly before us. We are kidding ourselves when we think that kind of statistics is vital to the welfare of the nation and the world. More and more, despite occasional appearances otherwise, we as academic statisticians are talking to ourselves. Even at this symposium we talk about how to do the old things better and more broadly, not about what we could offer to society, and what most needs to be done. Think about the whole range of the really big problems of the day: violence, crime and criminal justice, education and industrial productivity in the broadest senses, unemployment, the balance of trade, federal deficits, the health and welfare of millions of disadvantaged persons, urban rot, racial and ethnic tensions, homelessness, and many others. The kinds of statistics that we teach in undergraduate and especially in graduate programs have almost nothing to contribute to anything that matters on the scale of these problems. Instead, we teach about new abstractions in statistical theory, or we teach about new applications of theory to what are, in this context, tiny problems with tiny generalizations and tiny implications. I would certainly agree that every graduate student in statistics needs very substantial training in statistical theory, partly because of important applications, but partly also to recognize the many situations in which some theoretical approach is not appropriate. In my experience, the latter has been more common than the former. My own field, biostatistics, may illustrate a trend. Biostatistics began as an effort to break away from the sterile patterns of academic statistics, to develop and support practitioners who understand the problems and who contribute substantially to their solution. However, for many years biostatistics has been drifting back into this same kind of introspection, the navel gazing, the almost exclusive focus on theory, often theory that has no conceivable, important, real use. We fiddle while Rome bums. Then we wonder why the world passes us by. Why is it that the economists, for example, are often on the front pages of our newspapers, or testifying to Congress, or making major decisions about public policy, but not the statisticians? Is it that we do nothing on that scale of importance, nothing that merits that kind of attention? I fear that the answer is yes. We teach what we enjoy teaching and what we know how to teach, not what the world needs. Think about that litany of problem areas I just recited. The solutions to those problems could profit enormously from sound statistical data, soundly analyzed. But the difficulties that block our understanding on these problems have little to do with probability models or random variation, and everything to do with all those other good things that make up uncertainties, that is, what we broadly call bias. Bias dominates randomness almost everywhere. Think about your own past training and the training that many of you now deliver to new generations of students. What fraction of that training is or was devoted to bias? What fraction deals in any direct way with the big problems of this year? And when a statistician does take on the big problems and big programs, that person is very likely to be someone who has little or no formal statistical training. Ask yourself, for example, about the leadership of the big federal statistical agencies-somebody mentioned a dozen; I might count as many as 15. These include, for example, the Bureau of the Census, the National Center for Health Statistics, and the Bureau of Labor Statistics. How many of these 15 agencies have as directors someone who claims statistics as his or her primary discipline? I think of just two. How many directors would benefit from a deep understanding of statistics at the highest levels? Is it less than 15? I said that we talk about how we could do the old things better rather than how we might be doing the new things that need to be done. I discussed this matter briefly with Wayne Fuller of Iowa State University, who reminded me that if we go even further back in our history, our intellectual grandparents and great-grandparents in statistics did indeed devote themselves to these big problems, and good analysts found ways to use flawed and incomplete data to derive sound conclusions and practical recommendations. But that tradition has been allowed to slip into the hands of other disciplines. For example, it has been epidemiologists, rather than statisticians, who have spent much effort in recent years on two areas critical to statistical analysis. One is understanding the nature of confounding and the effects of efforts to reduce its influence. The other is developing a taxonomy of bias. This taxonomy has some very important, big, practical implications. Their work in both of these areas seems to be almost unknown to academic statisticians. Again, it is other disciplines that have tackled the thorny issues of teaching persons who may be highly expert in some area of application but have the equivalent of grade school training in statistics about the use, sometimes even the correct use, of powerful computational and inferential tools. We have not wanted to get our hands dirty. I believe that it was Ed Deming who first commented that we would never allow someone to teach surgery (or repair John C. Bailar III is Professor and Chair, Epidemiology and Biostatistics, McGill University, Montreal, PQ H3A 1A2, Canada. This is a revised version of an article presented at the National Research Council's Committee on Applied and Theoretical Statistics Symposium on Modern Interdisciplinary University Statistics Education at the 1993 Joint Statistical Meetings in San Francisco. It is reprinted from the symposium proceedings with the permission of the National Academy Press.

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