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Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. Intuitive Biostatistics, Completely Revised Second Edition, provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists.

Table of Contents

Preface

PART A. INTRODUCING STATISTICS

1. Statistics and Probability Are Not Intuitive

2. Why Statistics Can Be Hard to Learn

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. Relationship Between Confidence Intervals and Statistical Significance

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. Multiple Comparison Traps

24. Gaussian or Not?

25. Outliers

PART F. STATISTICAL TESTS

26. Comparing Observed and Expected Distributions

27. Comparing Proportions: Prospective and Experimental Studies

28. Comparing Proportions: 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. Models

35. Comparing Models

36. Nonlinear Regression

37. Multiple, Logistic, and Proportional Hazards Regression

38. Multiple Regression Traps

PART H. THE REST OF STATISTICS

39. Analysis of Variance

40. Multiple Comparison Tests After ANOVA

41. Nonparametric Methods

42. Sensitivity Specificity and Receiver-Operator Characteristic Curves

43. Sample Size

PART I. PUTTING IT ALL TOGETHER

44. Statistical Advice

45. Choosing a Statistical Test

46. Capstone Example

47. Review Problems

48. Answers to Review Problems

APPENDICES

A. Statistics with GraphPad

B. Statistics With Excel

C. Statistics R

D. Values of the t Distribution Needed to Compute CIs

E. A Review of Logarithms

References