This book is the result of a spirited debate stimulated by a recent meeting of the Society of Multivariate Experimental Psychology. Although the viewpoints span a range of perspectives, the overriding theme that emerges states that significance testing may still be useful if supplemented with some or all of the following -- Bayesian logic, caution, confidence intervals, effect sizes and power, other goodness of approximation measures, replication and meta-analysis, sound reasoning, and theory appraisal and corroboration.
The book is organized into five general areas. The first presents an overview of significance testing issues that sythesizes the highlights of the remainder of the book. The next discusses the debate in which significance testing should be rejected or retained. The third outlines various methods that may supplement current significance testing procedures. The fourth discusses Bayesian approaches and methods and the use of confidence intervals versus significance tests. The last presents the 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.
"The book is applauded for its comprehensive consideration of the pros and cons of statistical hypothesis testing (and alternatives) in psychological and educational research….editors Lisa Harlow, Stanley Mulaik, and James Steiger have--with aplomb, acumen, and even evenhandedness--assembled a 'wonderful' collection of essays on the pros, cons, and others of hypothesis testing….a book that belongs on every serious researcher's shelf. And so, with a final obligatory reviewer nod of priority to Gene and Roger, two thumbs up on this one--way up!"
—Educational and Psychological Measurement
"What If There Were No Significance Tests? is a thought-provoking book and worthy of the attention of anyone who is interested in the question of whether significance testing has a proper role to play in psychological research and, if so, what it is."
—Journal of Mathematical Psychology
"…the Harlow, Mulaik, and Steiger inaugural offering… should be required reading for every serious behavioral scientist, regardless of where a given scholar falls on the continuum of views of current statistical practice. The treatment is comprehensive, conceptually rich, and contemporary. No reader could study these chapters without being both challenged and stimulated."
Texas A&M University
"The most valuable part of the book here reviewed is its title. For teachers of statistics it offers some shock value. Teachers who jplace the logic of null hypothesis significance testing more or less on a par with scientific logic need to be awakened quite rudely; others can at least use the title to make students sit up and listen."
Contents: 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.