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Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking
OverviewIntuitive Biostatistics is both an introduction and review of statistics. Compared to other books, it has:Breadth rather than depth. It is a guidebook, not a cookbook.Words rather than math. It has few equations.Explanations rather than recipes. This book presents few details of statistical methods and only a few tables required to complete the...
OverviewIntuitive Biostatistics is both an introduction and review of statistics. Compared to other books, it has:Breadth rather than depth. It is a guidebook, not a cookbook.Words rather than math. It has few equations.Explanations rather than recipes. This book presents few details of statistical methods and only a few tables required to complete the calculations.Who is it for?I wrote Intuitive Biostatistics for three audiences:Medical (and other) professionals who want to understand the statistical portions of journals they read. These readers don't need to analyze any data, but need to understand analyses published by others.Undergraduate and graduate students, post-docs and researchers who will analyze data. This book explains general principles of data analysis, but it won't teach you how to do statistical calculations or how to use any particular statistical program.Scientists who consult with statisticians. Statistics often seems like a foreign language, and this text can serve as a phrase book to bridge the gap between scientists and statisticians.What's new in the second edition?Though the spirit of the first edition remains, very few of its words do. It is hard to explain what is new in this edition, since I essentially rewrote the entire book.New and expanded topics in the second edition of Intuitive Biostatistics include:Chapter 1 explains how our intuitions can lead us astray in issues of probability and statistics.Chapter 11 (and later examples) highlight the fact that lognormal distributions are common.Chapter 21 explains the idea of testing for equivalence vs. testing for differences.Chapters 22, 23, and 40 discuss the pervasive problem of multiple comparisons.Chapters 24 and 25 discuss testing for normality and for outliers.Chapter 35 shows how to think about statistical hypothesis testing as comparing the fits of alternative models.Chapters 37 and 38 give expanded coverage of the usefulness--and traps--of multiple, logistic, and proportional hazards regression.Chapter 43 briefly mentions adaptive study designs where sample size is not chosen in advance.Chapter 46 (inspired by, and written with, Bill Greco) reviews many topics in this book and more general issues of how to approach data analysis.
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