This paper summarizes the scientific activity of de Finetti in probability and statistics. It falls into three sections: Section 1 includes an essential biography of de Finetti and a survey of the basic features of the scientific milieu in which he took the first steps of his scientific career; Section 2 concerns de Finetti's work in probability: (a) foundations, (b) processes with independent increments, (c) sequences of exchangeable random variables, and (d) contributions which fall within other fields; Section 3 deals with de Finetti's contributions to statistics: (a) description of frequency distributions, (b) induction and statistics, (c) probability and induction, and (d) objectivistic schools and theory of decision. Many recent developments of de Finetti's work are mentioned here and briefly described.

The bioequivalence problem is of practical importance because the approval of most generic drugs in the United States and the European Community (EC) requires the establishment of bioequivalence between the brand-name drug and the proposed generic version. The problem is theoretically interesting because it has been recognized as one for which the desired inference, instead of the usual significant difference, is practical equivalence. The concept of intersection-union tests will be shown to clarify, simplify and unify bioequivalence testing. A test more powerful than the one currently specified by the FDA and EC guidelines will be derived. The claim that the bioequivalence problem defined in terms of the ratio of parameters is more difficult than the problem defined in terms of the difference of parameters will be refuted. The misconception that size-$\alpha$ bioequivalence tests generally correspond to $100(1 - 2 \alpha)%$ confidence sets will be shown to lead to incorrect statistical practices, and should be abandoned. Techniques for constructing $100(1 - \alpha)%$ confidence sets that correspond to size-$\alpha$ bioequivalence tests will be described. Finally, multiparameter bioequivalence problems will be discussed.

Flow cytometry is frequently used for assessing individual cell characteristic(s) for a large number of cells. It has a variety of medical applications including assessing the quantity of intracellular DNA and detecting the presence of antigens such as CD4. Flow cytometric variables are evaluated for their clinical prognostic value, particularly in cancer, and are often used for clinical screening of diseased patients. The prognostic worth of these variables is questionable and is controversial in the medical community. This controversy is caused in part by the multiple methods of analysis and the lack of adherence to quality control standards. The analysis of flow cytometric data presents a number of interesting statistical problems, particularly in deconvolution of overlapping distributions and detection of abnormal subpopulation(s) of cells. The current methods incorporate subjective procedures, may use ill-founded assumptions and yield differing results. This article summarizes the flow cytometry process of measurement and reviews unsolved statistical and quality control issues pertaining to the analysis of flow cytometric data. DNA histogram analysis is used to exemplify these issues.

Herman Chernoff was born in New York City on 1 July 1923. He went to school there and later received the B.S. degree from the City College of New York in 1943, majoring in mathematics with a minor in physics. For a year and a half, he worked as a junior physicist with the U.S. Navy, before joining Brown University for graduate work in applied mathematics. His studies were interrupted by a short period in the U.S. Army, and then his interest in statistics led him to complete his Ph.D. thesis at Columbia University under the supervision of Abraham Wald. At Brown University, Herman met Judy Ullman. They have been married since 1947 and have two daughters, Ellen and Miriam.

Herman worked for the Cowles Commission at the University of Chicago and then spent three years in the Mathematics Department at the University of Illinois before joining the Department of Statistics at Stanford University in 1952, where he remained for 22 years. He moved to M.I.T. in 1974, where he founded the Statistics Center. Since 1985 he has been in the Department of Statistics at Harvard.

Professor Chernoff has been honored for his contributions in many ways. He was President of the Institute of Mathematical Statistics and is an elected member of both the American Academy of Arts and Sciences and the National Academy of Sciences. The book Recent Advances in Statistics published in honor of his 60th birthday in 1983 contained papers in the fields where his influence as a researcher and teacher has been strong: design and sequential analysis, optimization and control, nonparametrics, large sample theory and statistical graphics.