Intuitive Biostatistics A Nonmathematical Guide to Statistical Thinking

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  • Edition: 3rd
  • Format: Paperback
  • Copyright: 12/13/2013
  • Publisher: Oxford University Press

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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.

Author Biography

Harvey Motulsky is the founder and CEO of GraphPad Software, Inc. The first edition of this book was written while he was on the faculty of the Department of Pharmacology at University of California, San Diego.

Table of Contents

Answers to Review Problems
PART A. Introducing Statistics
1. Statistics and Probability Are Not Intuitive
2. The Complexities of Probability
3. From Sample to Population
PART B. Introducing Confidence Intervals
4. Confidence Interval of a Proportion
5. Confidence Interval of Survival Data
6. Confidence Interval of Counted Data (Poisson Distribution)
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 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, 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
Answers to Review Problems

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