Product Review
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 StatisticsÂ
 1. Statistics and Probability Are Not Intuitive
 2. The Complexities of Probability
 3. From Sample to PopulationÂ
Part B: Confidence IntervalsÂ
 4. Confidence Interval of a ProportionÂ
 5. Confidence Interval of Survival DataÂ
 6. Confidence Interval of Counted DataÂ
Part C: Continuous VariablesÂ
 7. Graphing Continuous Data
 8. Types of VariablesÂ
 9. Quantifying ScatterÂ
10. The Gaussian DistributionÂ
11. The Lognormal Distribution and Geometric Mean
12. Confidence Interval of a MeanÂ
13. The Theory of Confidence Intervals
14. Error BarsÂ
PART D: P Values and SignificanceÂ
15. Introducing P ValuesÂ
16. Statistical Significance and Hypothesis Testing
17. Relationship Between Confidence Intervals and Statistical SignificanceÂ
18. Interpreting a Result That Is Statistically SignificantÂ
19. Interpreting a Result That Is Not Statistically SignificantÂ
20. Statistical Power
21. Testing for Equivalence or Noninferiority
PART E: Challenges in StatisticsÂ
22. Multiple Comparisons ConceptsÂ
23. The Ubiquity of Multiple Comparison
24. Normality Tests
25. OutliersÂ
26. Choosing a Sample Size
PART F: Statistical TestsÂ
27. Comparing Proportions
28. Case-Control Studies
29. Comparing Survival CurvesÂ
30. Comparing Two Means: Unpaired t Test
31. Comparing Two Paired Groups
32. CorrelationÂ
PART G: Fitting Models to DataÂ
33. Simple Linear Regression
34. Introducing ModelsÂ
35. Comparing ModelsÂ
36. Nonlinear Regression
37. Multiple RegressionÂ
38. Logistic and Proportional Hazards Regression
PART H The Rest of StatisticsÂ
39. Analysis of VarianceÂ
40. Multiple Comparison Tests After ANOVAÂ
41. Nonparametric Methods
42. Sensitivity and Specificity and Receiver-Operator Characteristic CurvesÂ
43. Meta-analysis
PART I Putting It All TogetherÂ
44. The Key Concepts of Statistics
45. Statistical Traps to Avoid
46. Capstone ExampleÂ
47. Review ProblemsÂ
48. Answers to Review ProblemsÂ
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