Table of Contents

Part A. Introducing Statistics
1. Statistics and Probability are not Intuitive
2. The Complexities of Probability
3. From Sample to Population

Part B. Introducing Confidence Intervals
4. Confidence Interval of a Proportion
5. Confidence Interval of Survival Data
6. Confidence Interval of Counted Data (Poisson Distribution)

Part C. Continuous Variables
7. Graphing Continuous Data
8. Types of Variables
9. Quantifying Scatter
10. The Gaussian Distribution
11. The Lognormal Distribution and Geometric Mean
12. Confidence Interval of a Mean
13. The Theory of Confidence Intervals
14. Error Bars

Part D. P Values and Statistical Significance
15. Introducing P Values
16. Statistical Significance and Hypothesis Testing
17. Comparing Groups with Confidence Intervals and P Values
18. Interpreting a Result That Is Statistically Significant
19. Interpreting a Result That Is Not Statistically Significant
20. Statistical Power
21. Testing For Equivalence or Noninferiority

Part E. Challenges in Statistics
22. Multiple Comparisons Concepts
23. The Ubiquity of Multiple Comparisons
24. Normality Tests
25. Outliers
26. Choosing a Sample Size

Part F. Statistical Tests
27. Comparing Proportions
28. Case-Control Studies
29. Comparing Survival Curves
30. Comparing Two Means: Unpaired t Test
31. Comparing Two Paired Groups
32. Correlation

Part G. Fitting Models to Data
33. Simple Linear Regression
34. Introducing Models
35. Comparing Models
36. Nonlinear Regression
37. Multiple Regression
38. Logistic and Proportional Hazards Regression

Part H. The Rest of Statistics
39. Analysis of Variance
40. Multiple Comparison Tests after ANOVA
41. Nonparametric Methods
42. Sensitivity, Specificity, and Receiver-Operating Characteristic Curves
43. Meta-Analysis

Part I. Putting It All Together
44. The Key Concepts of Statistics
45. Statistical Traps to Avoid
46. Capstone Example
47. Statistics and Reproducibility
48. Checklists for Reporting Statistical Methods and Results

Part J. Appendices

About the Author

Harvey Motulsky is the CEO and Founder of GraphPad Software, Inc. He wrote the first edition of this text while on the faculty of the Department of Pharmacology at University of California, San Diego.

Reviews

"This splendid book meets a major need in public health, medicine, and biomedical research training. It is a user-friendly biostatistics text for non-mathematicians that clearly explains how to make sense of statistical results, avoid common mistakes in data analysis, avoid being confused by statistical nonsense, and how to make research more reproducible. Students may enjoy statistics for the first time!"--Gilbert S. Omenn, University of Michigan
"This book applies what I would call "scientific common sense" to the confusing world of statistical analysis and interpretation. If you want to really understand what a p-value is, read this book." -- Louis G. Zachos, University of Mississippi
"Intuitive Biostatistics places statistical concepts and practical issues of data analysis within an understandable light. The textbook helps the reader grasp the fundamentals and the pitfalls of data presentation and analysis. It should be on the "must-read" list of clinicians, journal reviewers, editors, and other consumers of scientific literature." -- John D. Bonagura, The Ohio State University
"I have already recommend the book to many colleagues. A concise, well written, and at times funny book that clearly explains the most important conceptual aspects about statistics, emphasizing proper interpretation of results and common mistakes to avoid." -- Walter E. Schargel, The University of Texas at Arlington