Basic Concepts of Probability and Statistics in the Law
When as a practicing lawyer I published my ?rst article on statistical evidence in 1966, the editors of the Harvard Law Review told me that a mathematical equa- 1 tion had never before appeared in the review. This hardly seems possible - but if they meant a serious mathematical equation, perhaps they were right. Today all that has changed in legal academia. Whole journals are devoted to scienti?c methods in law or empirical studies of legal institutions. Much of this work involves statistics. Columbia Law School, where I teach, has a professor of law and epidemiology and other law schools have similar “law and” professorships. Many offer courses on statistics (I teach one) or, more broadly, on law and social science. The same is true of practice. Where there are data to parse in a litigation, stat- ticians and other experts using statistical tools now frequently testify. And judges must understand them. In 1993, in its landmark Daubert decision, the Supreme Court commanded federal judges to penetrate scienti?c evidence and ?nd it “re- 2 liable” before allowing it in evidence. It is emblematic of the rise of statistics in the law that the evidence at issue in that much-cited case included a series of epidemiological studies. The Supreme Court’s new requirement made the Federal Judicial Center’s Reference Manual on Scienti?c Evidence, which appeared at about the same time, a best seller. It has several important chapters on statistics.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
aggregation assumed assumption average Bayes’s theorem Bayesian Bendectin bias binomial distribution calculation cancer case–control studies causation caused coefficient Company B bus computed confidence interval correlation death defendant’s dependent variable difference discrimination disease disparity district effect employees equal equation error term evidence example expected number expected value expert explanatory factors explanatory variables exposure fact frequency geometric mean given harmonic mean Hispanic increase jury leukemia level of significance likelihood ratio M.O. Finkelstein match measure median multiple normal distribution null hypothesis observed odds ratio one-tailed outcome percentage plaintiffs population prediction prediction intervals prior probabilities prob proxy random sample random variable regression estimate regression model reject the null relative risk residuals result salary sample mean sampling error selected Springer Science+Business Media standard deviation standard error statistically significant statisticians Supreme Court testified tion toss trials usually variance variation votes weight women zero