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