Part A: Introducing Statistics
1. Statistics and Probability Are Not Intuitive
2. The Complexities of Probability
3. From Sample to Population
Part B: Confidence Intervals
4. Confidence Interval of a Proportion
5. Confidence Interval of Survival Data
6. Confidence Interval of Counted Data
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 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 and 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