Intuitive Biostatistics A Nonmathematical Guide to Statistical Thinking

Intuitive Biostatistics takes a non-technical, non-quantitative approach to statistics and emphasizes interpretation of statistical results rather than the computational strategies for generating statistical data. This makes the text especially useful for those in health-science fields who have not taken a biostatistics course before. The text is also an excellent resource for professionals in labs, acting as a conceptually oriented and accessible biostatistics guide. With an engaging and conversational tone, Intuitive Biostatistics provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists.

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.

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