Jerzy Neyman received the National Medal of Science "for laying the foundations of modern statistics and devising tests and procedures that have become essential parts of the knowledge of every statistician." Until his death in 1981 at the age of 87, Neyman was vigorously involved in the concerns and controversies of the day, a scientist whose personality and activity were integral parts of his contribution to science. His career is thus particularly well-suited for the non-technical life-story which Constance Reid has made her own in such well-received biographies of Hilbert and Courant. She was able to talk extensively with Neyman and have access to his personal and professional letters and papers. Her book will thus appeal to professional statisticians as well as amateurs wanting to learn about a subject which permeates almost every aspect of modern life.

Jerzy Neyman received the National Medal of Science "for laying the foundations of modern statistics and devising tests and procedures that have become essential parts of the knowledge of every statistician." Until his death in 1981 at the age of 87, Neyman was vigorously involved in the concerns and controversies of the day, a scientist whose personality and activity were integral parts of his contribution to science. His career is thus particularly well-suited for the non-technical life-story which Constance Reid has made her own in such well-received biographies of Hilbert and Courant. She was able to talk extensively with Neyman and have access to his personal and professional letters and papers. Her book will thus appeal to professional statisticians as well as amateurs wanting to learn about a subject which permeates almost every aspect of modern life.

Classical statistical theory—hypothesis testing, estimation, and the design of experiments and sample surveys—is mainly the creation of two men: Ronald A. Fisher (1890-1962) and Jerzy Neyman (1894-1981). Their contributions sometimes complemented each other, sometimes occurred in parallel, and, particularly at later stages, often were in strong opposition. The two men would not be pleased to see their names linked in this way, since throughout most of their working lives they detested each other. Nevertheless, they worked on the same problems, and through their combined efforts created a new discipline. This new book by E.L. Lehmann, himself a student of Neyman’s, explores the relationship between Neyman and Fisher, as well as their interactions with other influential statisticians, and the statistical history they helped create together. Lehmann uses direct correspondence and original papers to recreate an historical account of the creation of the Neyman-Pearson Theory as well as Fisher’s dissent, and other important statistical theories.

How do people search evidence for a hypothesis? A well documented answer in cognitive psychology is that they search for confirming evidence. However, the rational strategy is to try to falsify the hypothesis. This book critically evaluates this contradiction. Experimental research is discussed against the background of philosophical and formal theories of hypothesis testing with striking results: Falsificationism and verificationism - the two main rival philosophies of testing - come down to one and the same principle for concrete testing behaviour, eluding the contrast between rational falsification and confirmation bias. In this book, the author proposes a new perspective for describing hypothesis testing behaviour - the probability-value model - which unifies the contrasting views. According to this model, hypothesis testers pragmatically consider what evidence and how much evidence will convince them to reject or accept the hypothesis. They might either require highly probative evidence for its acceptance, at the risk of its rejection, or protect it against rejection and go for minor confirming observations. Interestingly, the model refines the classical opposition between rationality and pragmaticity because pragmatic considerations are a legitimate aspect of 'rational' hypothesis testing. Possible future research and applications of the ideas advanced are discussed, such as the modelling of expert hypothesis testing.

Few students sitting in their introductory statistics class learn that they are being taught the product of a misguided effort to combine two methods into one. Few students learn that some think the method they are being taught should be banned. Wise Use of Null Hypothesis Tests: A Practitioner’s Handbook follows one of the two methods that were combined: the approach championed by Ronald Fisher. Fisher’s method is simple, intuitive, and immune to criticism. Wise Use of Null Hypothesis Tests is also a user-friendly handbook meant for practitioners. Rather than overwhelming the reader with endless mathematical operations that are rarely performed by hand, the author of Wise Use of Null Hypothesis Tests emphasizes concepts and reasoning. In Wise Use of Null Hypothesis Tests, the author explains what is accomplished by testing null hypotheses—and what is not. The author explains the misconceptions that concern null hypothesis testing. He explains why confidence intervals show the results of null hypothesis tests, performed backwards. Most importantly, the author explains the Big Secret. Many—some say all—null hypotheses must be false. But authorities tell us we should test false null hypotheses anyway to determine the direction of a difference that we know must be there (a topic unrelated to so-called one-tailed tests). In Wise Use of Null Hypothesis Tests, the author explains how to control how often we get the direction wrong (it is not half of alpha) and commit a Type III (or Type S) error. Offers a user-friendly book, meant for the practitioner, not a comprehensive statistics book Based on the primary literature, not other books Emphasizes the importance of testing null hypotheses to decide upon direction, a topic unrelated to so-called one-tailed tests Covers all the concepts behind null hypothesis testing as it is conventionally understood, while emphasizing a superior method Covers everything the author spent 32 years explaining to others: the debate over correcting for multiple comparisons, the need for factorial analysis, the advantages and dangers of repeated measures, and more Explains that, if we test for direction, we are practicing an unappreciated and unnamed method of inference