Intuitive Biostatistics

Harvey Motulsky is the CEO and Founder of GraphPad Software, Inc. He wrote the first edition of this text while on the faculty of the Department of Pharmacology at University of California, San Diego.

• Part A. Introducing Statistics1. Statistics and Probability are not Intuitive2. The Complexities of Probability3. From Sample to PopulationPart B. Introducing Confidence Intervals4. Confidence Interval of a Proportion5. Confidence Interval of Survival Data6. Confidence Interval of Counted Data (Poisson Distribution)Part C. Continuous Variables7. Graphing Continuous Data8. Types of Variables9. Quantifying Scatter10. The Gaussian Distribution11. The Lognormal Distribution and Geometric Mean12. Confidence Interval of a Mean13. The Theory of Confidence Intervals14. Error BarsPart D. P Values and Statistical Significance15. Introducing P Values16. Statistical Significance and Hypothesis Testing17. Comparing Groups with Confidence Intervals and P Values18. Interpreting a Result That Is Statistically Significant19. Interpreting a Result That Is Not Statistically Significant20. Statistical Power21. Testing For Equivalence or NoninferiorityPart E. Challenges in Statistics22. Multiple Comparisons Concepts23. The Ubiquity of Multiple Comparisons24. Normality Tests25. Outliers26. Choosing a Sample SizePart F. Statistical Tests27. Comparing Proportions28. Case-Control Studies29. Comparing Survival Curves30. Comparing Two Means: Unpaired t Test31. Comparing Two Paired Groups32. CorrelationPart G. Fitting Models to Data33. Simple Linear Regression34. Introducing Models35. Comparing Models36. Nonlinear Regression37. Multiple Regression38. Logistic and Proportional Hazards RegressionPart H. The Rest of Statistics39. Analysis of Variance40. Multiple Comparison Tests after ANOVA41. Nonparametric Methods42. Sensitivity, Specificity, and Receiver-Operating Characteristic Curves43. Meta-AnalysisPart I. Putting It All Together44. The Key Concepts of Statistics45. Statistical Traps to Avoid46. Capstone Example47. Statistics and Reproducibility48. Checklists for Reporting Statistical Methods and ResultsPart J. Appendices

This splendid book meets a major need in public health, medicine, and biomedical research training. It is a user-friendly biostatistics text for non-mathematicians that clearly explains how to make sense of statistical results, avoid common mistakes in data analysis, avoid being confused by statistical nonsense, and how to make research more reproducible. Students may enjoy statistics for the first time!