Routledge What If There Were No Significance Tests?: Classic Edition (Multivariate Applications Series)

The classic edition of What If There Were No Significance Tests? highlights current statistical inference practices. Four areas are featured as essential for making inferences: sound judgment, meaningful research questions, relevant design, and assessing fit in multiple ways. Other options (data visualization, replication or meta-analysis), other features (mediation, moderation, multiple levels or classes), and other approaches (Bayesian analysis, simulation, data mining, qualitative inquiry) are also suggested. The Classic Edition’s new Introduction demonstrates the ongoing relevance of the topic and the charge to move away from an exclusive focus on NHST, along with new methods to help make significance testing more accessible to a wider body of researchers to improve our ability to make more accurate statistical inferences. Part 1 presents an overview of significance testing issues. The next part discusses the debate in which significance testing should be rejected or retained. The third part outlines various methods that may supplement significance testing procedures. Part 4 discusses Bayesian approaches and methods and the use of confidence intervals versus significance tests. The book concludes with philosophy of science perspectives. Rather than providing definitive prescriptions, the chapters are largely suggestive of general issues, concerns, and application guidelines. The editors allow readers to choose the best way to conduct hypothesis testing in their respective fields. For anyone doing research in the social sciences, this book is bound to become "must" reading. Ideal for use as a supplement for graduate courses in statistics or quantitative analysis taught in psychology, education, business, nursing, medicine, and the social sciences, the book also benefits independent researchers in the behavioral and social sciences and those who teach statistics. Review "What If There Were No Significance Tests was a book ahead of its time. ... It inspired me to start teaching statistics differently. … It continues to have an impact on me. I return to it every time debates erupt about significance testing, confidence intervals, or statistical inference generally. Nearly two decades after its publication, many of its ideas still are not only timely, but in some cases, unsurpassed. If there is any book in psychology’s methodological canon that deserves a classic republication, this is it." – Michael Smithson, the Australian National University, Australia "The contributors are a "Who’s Who" of specialists in a variety of statistical areas. The book provides a balanced account of one of the most controversial and important issues of data analysis in recent decades, and it has inspired countless important researches and articles on such topics as significance testing, estimation of effect sizes, and construction of confidence intervals. Instruction in statistics has, or should be, greatly influenced by this book." – Robert Grissom, San Francisco State University, USA "The book remains the sourcebook for issues related to Null Hypothesis Significance Testing and its alternatives. … This is the go-to book for information on significance testing and its ramifications. ... Strengths and limitations of significance testing are entertainingly described by leaders in methodology. The comments in the book are as relevant today as ever." – David P. Mackinnon, Arizona State University, USA About the Author Lisa L. Harlow is Professor of Psychology at the University of Rhode Island. She is the Editor of Psychological Methods and a past president of the American Psychological Association‘s Division 5.Stanley A. Mulaik is Professor Emeritus of Psychology at Georgia Institute of Technology. His research interests include philosophy of statistics and causality and objectivity.James H. Steiger is Professor of Psychology and Human Development at Vanderbilt University. His research interests include the use of confi