Prove It With Figures displays some of the tools of the social and statistical sciences that have been applied in the courtroom and to the study of questions of legal importance. It explains how researchers can extract the most valuable and reliable data that can conveniently be made available, and how these efforts sometimes go awry. In the tradition of Zeisel's standard work "Say It with Figures," the authors clarify, in non-technical language, some of the basic problems common to all efforts to discern cause-and-effect relationships. Designed as a textbook for law students who seek an appreciation of the power and limits of empirical methods, this is also a useful reference for lawyers, policymakers, and members of the public who would like to improve their critical understanding of the statistics presented to them. The many case histories include analyses of the death penalty, jury selection, employment discrimination, mass torts, and DNA profiling.

This volume presents a rigorous account of statistical forecasting efforts that led to the successful resolution of the Johns-Manville asbestos litigation. This case, taking 12 years to reach settlement, is expected to generate nearly 500,000 claims at a total nominal value of over $34 billion. The forecasting task, to project the number, timing, and nature of claims for asbestos-related injuries from a set of exposed persons of unknown size, is a general problem: the models in this volume can be adapted to forecast industry-wide asbestos liability. More generally, because the models are not overly dependent on the U.S. legal system and the role of asbestos as a dangerous/defective product, this volume will be of interest in other product liability cases, as well as similar forecasting situations for a range of insurable or compensable events. The volume stresses the iterative nature of model building and the uncertainty generated by lack of complete knowledge of the injury process. This uncertainty is balanced against the Court's need for a definitive settlement, and the volume addresses how these opposing principles can be reconciled. The volume is written for a broad audience of actuaries, biostatisticians, demographers, economists, epidemiologists, environmental health scientists, financial analysts, industrial-risk analysts, occumpational health analysts, product liability analysts, and statisticians. The modest prerequisites include basic concepts of statistics, calculus, and matrix algebra. Care is taken that readers without specialized knowledge in these areas can understand the rationale for specific applications of advanced methods. As a consequence, this volume will be an indispensable reference for all whose work involves these topics. Eric Stallard, A.S.A., M.A.A.A., is Research Professor and Associate Director of the Center for Demographic Studies at Duke University. He is a Member of the American Academy of Actuaries and an Associate of the Society of Actuaries. He serves on the American Academy of Actuaries Committees on Long Term Care and Social Insurance. He also serves on the society of Actuaries' Long Term Care Experience Committee. His research interests include modelling and forecasting for medical demography and health actuarial practice. He was the 1996 winner of the National Institute on Aging's James A. Shannon Director's Award. Kenneth G. Manton, Ph.D., is Research Professor, Research Director, and Director of the Center for Demographic Studies at Duke University and Medical Research Professor at Duke University Medical Center's Department of Community and Family Medicine. Dr. Manton is also a Senior Fellow of the Duke University Medical Center's Center for the Study of Aging and Human Development. His research interests include mathematical models of human aging, mortality, and chronic disease. He was the 1990 recipient of the Mindel C. Sheps Award in Mathematical Demography presented by the Population Association of America; and in 1991 he received the Allied-Signal Inc. Achievement Award in Aging administred by the Johns Hopkins Center on Aging. Joel E. Cohen, Ph.D., Dr. P.H., is Professor of Populations, and Head of the Laboratory of Populations, Rockefeller University. He also is Professor of Populations at Columbia University. His research interests include the demography, ecology, epidemiology, and social organization of human and non-human populations, and related mathematical concepts. In 1981, he was elected Fellow of the MacArthur and Guggenheim Foundations. He was the 1992 recipient of the Mindel C. Sheps Award in Mathematical Demography presented by the Population Association of America; and in 1994, he received the Distinguished Statistical Ecologist Award at the Sixth International Congress of Ecology.

Prove It With Figures displays some of the tools of the social and statistical sciences that have been applied in the courtroom and to the study of questions of legal importance. It explains how researchers can extract the most valuable and reliable data that can conveniently be made available, and how these efforts sometimes go awry. In the tradition of Zeisel's standard work "Say It with Figures," the authors clarify, in non-technical language, some of the basic problems common to all efforts to discern cause-and-effect relationships. Designed as a textbook for law students who seek an appreciation of the power and limits of empirical methods, this is also a useful reference for lawyers, policymakers, and members of the public who would like to improve their critical understanding of the statistics presented to them. The many case histories include analyses of the death penalty, jury selection, employment discrimination, mass torts, and DNA profiling.

This selection of papers encompasses recent methodological advances in several important areas, such as multivariate failure time data and interval censored data, as well as innovative applications of the existing theory and methods. Using a rigorous account of statistical forecasting efforts that led to the successful resolution of the John-Manville asbestos litigation, the models in this volume can be adapted to forecast industry-wide asbestos liability. More generally, because the models are not overly dependent on the U.S. legal system and the role of asbestos, this volume will be of interest in other product liability cases, as well as similar forecasting situations for a range of insurable or compensational events. Throughout the text, the emphasis is on the iterative nature of model building and the uncertainty generated by lack of complete knowledge of the injury process. This uncertainty is balanced against the court's need for a definitive settlement, and how these opposing principles can be reconciled. A valuable reference for researchers and practitioners in the field of survival analysis.