Intuitive 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. I’ve tried to explain the big picture, without getting bogged down in too many details.
- 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. It makes a great companion to the more traditional statistics texts and to the documentation of statistical software.
- 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. Sprinkled throughout the book are “Lingo” sections that explain statistical terminology, and point out when statistics gives ordinary words very specialized meanings (the source of much confusion).
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. If you own the first edition of Intuitive Biostatistics and wonder whether this edition is different enough to justify buying it, the answer is yes! I have added new chapters, expanded coverage of some topics that were only touched upon in the first edition, and reorganized everything.
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.
About the author
After graduating from medical school and doing an internship in internal medicine, I switched to research in receptor pharmacology (and published over 50 peer reviewed articles). While I was on the faculty of the Department of Pharmacology at the University of California San Diego, I was given the job of teaching statistics to first year medical students and to graduate students. The syllabus for those courses grew into the first edition of this book. I hated creating graphs by hand, so I created some programs to do so. I also created some simple statistics programs after realizing that the existing statistical software, while great for statisticians, was overkill for most scientists. These efforts were the origins of GraphPad Software Inc., which has been a full-time endeavor for many years. In this role I email with students and scientists almost daily, making me acutely aware of the many ways that statistical concepts can be confusing or misunderstood.