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 third edition?
This third edition is not a complete rewrite, but I have substantially edited nearly every chapter to clarify concepts and add examples. Some chapters were essentially rewritten. Additionally, the third edition has these improvements:
- New chapter 2, addressing the complexities of probability. Statistical thinking is based on understanding the basic concepts of probability, so this chapter explains the basics.
- New chapter 43, addressing meta-analysis. Combining the results of many studies, or meta-analysis, is becoming more and more common. This chapter helps you understand a published meta-analysis.
- New chapter 45, reviewing statistical traps to avoid. Chapter 45 of the previous edition, which explained how to choose a statistical test, is now Appendix F.
- More examples. New examples have been added to seven chapters in order to better illustrate the specific, real-world applications of statistical topics.
- Reorganized chapters. Six chapters of the previous edition have been extensively rewritten. Chapter 23 has been reorganized and expanded to emphasize the many ways in which multiple comparisons occur. Chapter 26, about choosing sample size, is expanded and reorganized from the former chapter 43. Chapter 27 has been modified to include material from the previous edition’s chapter 26. Chapters 37 (multiple regression) and 38 (logistic regression) have been rethought and expanded. Chapter 44 has been pared down to a concise summary of the important points of statistics.
- Emphasis on how to avoid common mistakes. About half the chapters have sections on common mistakes.
- Summary of terminology. Each chapter now ends with a list of terms introduced in that chapter. Much of the challenge in learning statistics is learning the terminology, and these lists will make that process easier.
- End of chapter summaries. Each chapter now ends with a list of the most important points you should remember.
- More Q&A sections. The question and answer sections of the second edition were popular, so I’ve now included one at the end of almost every chapter.
- More figures. The new edition now includes fifteen new figures, ten of which appear in the new chapter on statistical traps to avoid.
- More topics. New to this edition are discussions of pseudoreplicates, genome-wide association studies, primary versus secondary outcomes in clinical trials, researcher degrees of freedom, P-hacking, multiplicity-adjusted P values, five-sigma cutoff, reproducibility of P values, the relationship between sample size and P value, five-number summary, Type S errors, and more.
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.