Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: 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. Intuitive Biostatistics, Completely Revised Second Edition, provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists.
New to this Edition:
• Chapter 1 shows how our intuitions lead us to misinterpret data, thus
explaining the need for statistical rigor.
• Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books.
• Chapter 21 contrasts testing for equivalence with testing for differences.
• Chapters 22, 23, and 40 explore the pervasive problem of multiple comparisons.
• Chapters 24 and 25 review testing for normality and outliers.
• Chapter 35 shows how statistical hypothesis testing can be understood as comparing the fits of alternative models.
• Chapters 37 and 38 provide a brief introduction to multiple, logistic, and proportional hazards regression.
• Chapter 46 reviews one example in great depth, reviewing numerous statistical concepts and identifying common mistakes.
• Chapter 47 includes 49 multi-part problems, with answers fully discussed in Chapter 48.
• New "Q and A" sections throughout the book review key concepts.
Table of Contents
PART A. INTRODUCING STATISTICS
1. Statistics and Probability Are Not Intuitive
2. Why Statistics Can Be Hard to Learn
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. Multiple Comparison Traps
24. Gaussian or Not?
PART F. STATISTICAL TESTS
26. Comparing Observed and Expected Distributions
27. Comparing Proportions: Prospective and Experimental Studies
28. Comparing Proportions: Case-Control Studies
29. Comparing Survival Curves
30. Comparing Two Means: Unpaired t test
31. Comparing Two Paired Groups
PART G. FITTING MODELS TO DATA
33. Simple Linear Regression
35. Comparing Models
36. Nonlinear Regression
37. Multiple, Logistic, and Proportional Hazards Regression
38. Multiple Regression Traps
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. Sample Size
PART I. PUTTING IT ALL TOGETHER
44. Statistical Advice
45. Choosing a Statistical Test
46. Capstone Example
47. Review Problems
48. Answers to Review Problems
A. Statistics with GraphPad
B. Statistics With Excel
C. Statistics R
D. Values of the t Distribution Needed to Compute CIs
E. A Review of Logarithms