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Hardcover How Many Subjects?: Statistical Power Analysis in Research Book

ISBN: 0803929498

ISBN13: 9780803929494

How Many Subjects?: Statistical Power Analysis in Research

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Format: Hardcover

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Book Overview

With increased emphasis on helping readers understand the context in which power calculations are done, this Second Edition introduces a simple technique of statistical power analysis that allows... This description may be from another edition of this product.

Customer Reviews

2 ratings

significance and power

Whatever your statistical background, the approach of this book to the question of statistical power is highly illuminating. For instance, I am a poor statistician, primarily an engineer with a mathematical bent. I happen to fall into experimental and, thus, statistical contexts frequently. Nevertheless, this book elucidated the issues surrounding the somewhat ritualistic "receiver operating characteristics" tradeoffs routine of detectability versus false alarm rate more than any text in my experience. It showed how costs of failures factored into the decision threshold, rather than making some routine mimickry of a classroom derivation. So even if statistical power isn't a routine place to visit, this book is worthwhile. I'd say, however, that matters of statistical power ought to be considered more than they are, even if they aren't a natural fit for Bayesian formulations, which seem currently to be in vogue.

simple well-written book about sample size determination

Helen Kraemer is a Stanford Professor with a subspecialty in statistics. She wrote this delightful book with the simple title "How many subjects." Along with Jacob Cohen's book this was one of the first statistical textbooks devoted to the determination of power and sample size in hypothesis testing situations. It is a great reference book on the topic. With the appearance of statistical software to handle the problems addressed in these books and more, the book now serve the purpose of providing a deeper understanding of the issues and an aid to help the user understand what the parameters (e.g. effect size, subject to subject variance) mean.
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