This reference has been designed for statisticians engaged as expert witnesses in legal proceedings. Examining the relationship between statistical methods and the law, it describes a number of cases during which the analysis of statistics was an important factor in determining the outcome.

How to conduct clinical trials in an ethical and scientifically responsible manner

This book presents a methodology for clinical trials that produces improved health outcomes for patients while obtaining sound and unambiguous scientific data. It centers around a real-world test case--involving a treatment for hypertension after open heart surgery--and explains how to use Bayesian methods to accommodate both ethical and scientific imperatives.

The book grew out of the direct involvement in the project by a diverse group of experts in medicine, statistics, philosophy,...

This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for Bayesian statistics. The volume will be particularly valuable to philosophers concerned with decision theory, probability, and statistics, statisticians, mathematicians, and economists.

Statistics in the Law is primarily a user's manual or desk reference for the expert witness-lawyer team and, secondarily, a textbook or supplemental textbook for upper level undergraduate statistics students. It starts with two articles by masters of the trade, Paul Meier and Franklin Fisher. It then explains the distinction between the Frye and Daughbert standards for expert testimony, and how these standards play out in court. The bulk of the book addresses individual cases covering a wide variety of questions, including:

-Does electronic draw poker require skill to play?...

A fair question to ask of an advocate of subjective Bayesianism (which the author is) is "how would you model uncertainty?" In this book, the author writes about how he has done it using real problems from the past, and offers additional comments about the context in which he was working.