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Provides a non-mathematical introduction to statistics for graduate students in the biological sciences, with an emphasis on interpreting results rather than on actually performing the tests themselves.
Part A: Introducing Statistics
1. Statistics and Probability Are Not Intuitive 3
2. Why Statistics Can Be Hard to Learn 14
3. From Sample to Population 17
Part B: Confidence Intervals
4. Confidence Interval of a Proportion 25
5. Confidence Interval of Survival Data 38
6. Confidence Interval of Counted Data 47
Part C: Continuous Variables
7. Graphing Continuous Data 57
8. Types of Variables 67
9. Quantifying Scatter 71
10. The Gaussian Distribution 78
11. The Lognormal Distribution and Geometric Mean 83
12. Confidence Interval of a Mean 87
13. The Theory of Confidence Intervals 96
14. Error Bars 103
PART D: P Values and Significance
15. Introducing P Values 111
16. Statistical Significance and Hypothesis Testing 122
17. Relationship Between Confidence Intervals and Statistical Significance 130
18. Interpreting a Result That Is Statistically Significant 134
19. Interpreting a Result That Is Not Statistically Significant 141
20. Statistical Power 146
21. Testing for Equivalence or Noninferiority 150
PART E: Challenges in Statistics
22. Multiple Comparisons Concepts 159
23. Multiple Comparison Traps 168
24. Gaussian or Not? 175
25. Outliers 181
PART F: Statistical Tests
26. Comparing Observed and Expected Distributions 191
27. Comparing Proportions: Prospective and Experimental Studies 196
28. Comparing Proportions: Case-Control Studies 203
29. Comparing Survival Curves 210
30. Comparing Two Means: Unpaired t Test 219
31. Comparing Two Paired Groups 231
32. Correlation 243
PART G: Fitting Models to Data
33. Simple Linear Regression 255
34. Introducing Models 270
35. Comparing Models 276
36. Nonlinear Regression 285
37. Multiple, Logistic, and Proportional Hazards Regression 296
38. Multiple Regression Traps 315
PART H The Rest of Statistics 321
39. Analysis of Variance 323
40. Multiple Comparison Tests After ANOVA 331
41. Nonparametric Methods 344
42. Sensitivity and Specificity and Receiver-Operator Characteristic Curves 354
43. Sample Size 363
PART I Putting It All Together 375
44. Statistical Advice 377
45. Choosing a Statistical Test 387
46. Capstone Example 390
47. Review Problems 406
48. Answers to Review Problems 418
Appendices
A. Statistics With GraphPad 451
B. Statistics With Excel 456
C. Statistics With R 458
D. Values of the t Distribution Needed to Compute CIs 460
E. A Review of Logarithms 462
"I am entranced by the book. Statistics is often difficult for many scientists to fully appreciate. Your writing style and explanation makes the concepts accessible."
– Tim Bushnell, Director of Flow Cytometry, Univ. Rochester Med. Center
"The second edition of Intuitive Biostatistics is a substantial improvement. I am particularly impressed by the chapters on multiple comparisons. This is increasingly important for such molecular trickery as gene expression chips, which produce a very large number of possible comparisons. Individual comparisons and even a Bonferroni correction are often inadequate. Motulsky deals with a newer method, false discovery rate (FDR), in a clear, understandable way. I'll be recommending the new edition with even greater enthusiasm."
– James F. Crow, University of Wisconsin
"This splendid book meets a major need in public health, medicine, and biomedical research training – a user-friendly biostatistics text for non-mathematicians that clearly explains how to make sense of statistical results and how to avoid being confused by statistical nonsense. You may enjoy statistics for the first time!"
– Gilbert S. Omenn, Professor of Medicine, Genetics, Public Health, and Computational Medicine & Bioinformatics, University of Michigan
