Intuitive Biostatistics: A Nonmathematical Guide To Statistical Thinking, 3Rd Edition

  • Publish Date: 2013-12-13
  • Binding: Paperback
  • Author: Harvey Motulsky

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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.


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. 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. The Ubiquity of Multiple Comparison
24. Normality Tests
25. Outliers
26. Choosing a SampleSize
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 HazardsRegression
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-Operator 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. Review Problems
48. Answers to Review Problems

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