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.
"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
New Introduction. Preface. Part I: Overview. L.L. Harlow, Significance Testing Introduction and Overview. Part II: The Debate: Against and For Significance Testing. J.Cohen, The Earth Is Round. F.L. Schmidt, J. Hunter, Eight Objections to the Discontinuation of Significance Testing in the Analysis of Research Data. S.A. Mulaik, N.S. Raju, R. Harshman, There Is a Time and Place for Significance Testing. R.P. Abelson, A Retrospective on the Significance Test Ban of 1999 (If There Were No Significance Tests, They Would Be Invented). Part III:Suggested Alternatives to Significance Testing. R.J. Harris, Reforming Significance Testing via Three-Valued Logic. J.S. Rossi, Spontaneous Recovery of Verbal Learning: A Case Study in the Failure of Psychology as a Cumulative Science. J.H. Steiger, R.T. Fouladi,Noncentrality Interval Estimation and the Evaluation of Statistical Models. R.P. McDonald,Goodness of Approximation in the Linear Model. Part IV: A Bayesian Approach to Hypothesis Testing. R.M. Pruzek, An Introduction to Bayesian Inference and Its Application.D. Rindskopf, Testing 'Small,' Not Null, Hypotheses: Classical and Bayesian Approaches.C.S. Reichardt, H.F. Gollob, When Confidence Intervals Should Be Used Instead of Statistical Significance Tests, and Vice Versa. Part V: Philosophy of Science Issues. W.W. Rozeboom, Good Science Is Abductive, Not Hypothetico-Deductive. P.E. Meehl, The Problem Is Epistemology, Not Statistics: Replace Significance Tests by Confidence Intervals and Quantify Accuracy of Risky Numerical Predictions.
This series of books offers highly accessible and widely applicable methodological topics that have broad appeal and are written in easy-to understand language. Sponsored by the Society of Multivariate Experimental Psychology http://www.smep.org/, it welcomes methodological applications from a variety of disciplines, such as psychology, public health, sociology, education, and business. Authored or edited volumes should feature one of several approaches:
Interested persons should e-mail: Lisa L. Harlow at LHarlow@uri.edu.