Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition

Thoroughly revised and updated, the third edition of Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking retains and refines the core perspectives of the previous editions: 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.

With its engaging and conversational tone, this unique book provides a clear introduction to statistics for undergraduate and graduate students in a wide range of fields and also serves as a statistics refresher for working scientists. It is especially useful for those students in health-science related fields who have no background in biostatistics.

CONTENTS
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 Mean12. Confidence Interval of a Mean 13. The Theory of Confidence Intervals14. Error Bars PART D: P Values and Significance 15. Introducing P Values 16. Statistical Significance and Hypothesis Testing17. 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 Power21. Testing for Equivalence or NoninferiorityPART E: Challenges in Statistics 22. Multiple Comparisons Concepts 23. The Ubiquity of Multiple Comparison24. Normality Tests25. Outliers 26. Choosing a Sample SizePART F: Statistical Tests 27. Comparing Proportions28. Case-Control Studies29. Comparing Survival Curves 30. Comparing Two Means: Unpaired t Test31. Comparing Two Paired Groups32. Correlation PART G: Fitting Models to Data 33. Simple Linear Regression34. Introducing Models 35. Comparing Models 36. Nonlinear Regression37. Multiple Regression 38. Logistic and Proportional Hazards RegressionPART H The Rest of Statistics 39. Analysis of Variance 40. Multiple Comparison Tests After ANOVA 41. Nonparametric Methods42. Sensitivity and Specificity and Receiver-Operator Characteristic Curves 43. Meta-analysisPART I Putting It All Together 44. The Key Concepts of Statistics45. Statistical Traps to Avoid46. Capstone Example 47. Review Problems 48. Answers to Review Problems