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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.
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