In Memory of David A. Freedman

In Memory of David A. Freedman


David A. Freedman, professor of statistics at the University of California, Berkeley, died of bone cancer in his home in Berkeley on 17 October 2008, at age 70.

Freedman was a fellow of the Institute of Mathematical Statistics and the American Statistical Association and a member of the American Academy of Arts and Sciences. He won the 2003 John J. Carty Award for the Advancement of Science from the National Academy of Sciences "for his profound contributions to the theory and practice of statistics, including rigorous foundations for Bayesian inference and trenchant analysis of census adjustment." He was a Fellow at the Miller Institute for Basic Research in Science in 1990, an Alfred P. Sloan Foundation Fellow in 1964–66, and a Canada Council Fellow at Imperial College London in 1960–61.

Freedman was born in Montreal, Canada, on 5 March 1938. He received a B.Sc. from McGill University in 1958 and a M.A. and a Ph.D. from Princeton University in 1959 and 1960, respectively. He joined the UC Berkeley Department of Statistics in 1961 as a lecturer and was appointed to the research faculty in 1962. He remained at Berkeley his entire career. He started his professional life as a probabilist and mathematical statistician with Bayesian leanings but became one of the world's leading applied statisticians and a circumspect frequentist. In his words:

My own experience suggests that neither decision-makers nor their statisticians do in fact have prior probabilities. A large part of Bayesian statistics is about what you would do if you had a prior. For the rest, statisticians make up priors that are mathematically convenient or attractive. Once used, priors become familiar; therefore, they come to be accepted as ‘natural’ and are liable to be used again; such priors may eventually generate their own technical literature … Similarly, a large part of [frequentist] statistics is about what you would do if you had a model; and all of us spend enormous amounts of energy finding out what would happen if the data kept pouring in. (Freedman, D.A., 1995. Some Issues in the Foundations of Statistics, Foundations of Science, 1, pp. 19–39.)

Freedman continued to work on probability and foundational issues, such as the limits of statistical methodology, the virtues of well designed observational studies, techniques for causal inference, and the interpretation of probability. He made major contributions to theoretical and applied statistics, statistical education, and statistics in law and public policy. His written legacy includes six books, 200 papers and 20 technical reports. He advanced the theory of the bootstrap, martingale inequalities, Markov Chains, de Finetti's Theorem, hierarchical Bayes models, the use of regression to analyze experimental data, and other topics. He studied whether adjusting the census for undercount would improve its accuracy, whether earthquake forecasts make sense, whether hormone replacement therapy saves lives, whether the Swine flu vaccine causes Guillain-Barré syndrome, and whether high salt intake causes hypertension, among other scientific questions. A hallmark of his research is painstaking attention to all aspects of a problem—including experimental design, data collection, statistical methodology, and mathematics.

By his own account, Freedman's transition to applied work was in part a response to the challenge of undergraduate teaching. On observing that students were not inspired by the stylized examples in textbooks, Freedman ferreted out compelling illustrations of basic statistical issues in the primary literature of a wide range of fields. One of the capstones of that exploration is the highly regarded undergraduate text, Statistics, co-authored with Robert Pisani and Roger Purves. That book was a landmark when it was published in 1978 and continues to be highly regarded and influential in its fourth edition. Built on serious examples from economics, epidemiology, medicine, and social science, it is meticulously accurate, emphasizing statistical thinking over formulae.

Berkeley was long famous for statistical theory; less so for applied work. Recognizing the fertility of the interplay of theory and scientific applications, Freedman helped transform the department into a powerhouse for mathematically informed applied work. Freedman was instrumental in recruiting Leo Breiman, Jack Kiefer, and Charles Stone and in acquiring the department's first computer, which he made usable. While department chair from 1981 to 1986, Freedman reorganized the undergraduate program to emphasize applied statistics. He regularly taught a graduate course in statistical consulting and for many years supervised the Statistical Consulting Service, which continues to serve campus researchers in a broad spectrum of disciplines and to provide real-world experience for statistics graduate students.

Freedman was a consulting or testifying expert on statistics in disputes involving employment discrimination, fair loan practices, voting rights, duplicate signatures on petitions, railroad taxation, ecological inference, flight patterns of golf balls, price scanner errors, Bovine Spongiform Encephalopathy (Mad Cow disease), and sampling. He consulted for the Bank of Canada, the Carnegie Commission, the City of San Francisco, the County of Los Angeles, and the Federal Reserve, as well as the U.S. departments of energy, treasury, justice, and commerce. Freedman and his colleague Kenneth Wachter testified to Congress and the courts against adjusting the 1980 and 1990 censuses using estimates of differential undercounts. A 1990 lawsuit that sought to compel the Department of Commerce to adjust the census was heard on appeal by the U.S. Supreme Court, which ruled unanimously in favor of the Commerce Department and Freedman and Wachter's analysis. With David Kaye, Freedman wrote a widely used primer on statistics for lawyers and judges published by the Federal Judicial Center, the education and research agency for the Federal courts.

In addition to his work in forensic statistics, Freedman had a broad impact on the application of statistics to important medical, social, and public policy issues, such as clinical drug trials, epidemiological studies, economic models, and the interpretation of scientific experiments and observational studies. In his applied work, Freedman emphasized exposing and checking the assumptions that underlie standard methods, as well as understanding how those methods behave when the assumptions are false. He characterized circumstances in which the methods continue to perform well, and those where they break down—regardless of the quality of the data.

Freedman is survived by his wife, Janet Macher; stepmother, Charlotte Freedman of Montreal, Canada; children Joshua of Corralitos, CA, and Deborah Freedman Lustig of Walnut Creek, CA; his first wife, Shanna Helen (Wittenberg) Swan of Rochester, NY; and four grandchildren.

Donations in memory of David A. Freedman may be made to the UC Berkeley Foundation, c/o University Relations, 2080 Addison St., Berkeley, CA 94720-4200.

Prof. Freedman's website, contains links to many of his papers and books. It will continue to be maintained.

By P. B. Stark, originally at