Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking / Edition 2

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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.


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.

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Editorial Reviews

From the Publisher
"Intuitive Biostatistics is a beautiful book that has much to teach experimental biologists of all stripes. Motulsky has written thoughtfully, with compelling logic and wit. He teaches by example what one may expect of statistical methods and, perhaps just as importantly, what one may not expect of them. He is to be congratulated for this work, which will surely be valuable and perhaps even transformative for many of the scientists who read it."—Bruce Beutler, 2011 Nobel Laureate, Physiology or Medicine, and Director, Center for the Genetics of Host Defense, UT Southwestern Medical Center

"Let's face it. Most statistics textbooks intimidate the average student. Motulsky's Intuitive Biostatistics, however, is written in a welcoming tone. It takes the static out of statistics. This textbook covers a wide spectrum of statistical concepts in a way that will benefit readers with varying levels of quantitative backgrounds."—Heather Hoffman, George Washington University

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Product Details

  • ISBN-13: 9780199730063
  • Publisher: Oxford University Press
  • Publication date: 1/20/2010
  • Edition description: Older Edition
  • Edition number: 2
  • Pages: 508
  • Product dimensions: 6.10 (w) x 9.20 (h) x 0.80 (d)

Meet the Author

Previously held faculty position in the Department of Pharmacology at the University of California San Diego. Currently, founder of GraphPad Software, Inc.

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Table of Contents

1. Statistics and Probability Are Not Intuitive
2. Why Statistics Can Be Hard to Learn
3. From Sample to Population
4. Confidence Interval of a Proportion
5. Confidence Interval of Survival Data
6. Confidence Interval of Counted Data
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
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
22. Multiple Comparisons Concepts
23. Multiple Comparison Traps
24. Gaussian or Not?
25. Outliers
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
32. Correlation
33. Simple Linear Regression
34. Models
35. Comparing Models
36. Nonlinear Regression
37. Multiple, Logistic, and Proportional Hazards Regression
38. Multiple Regression Traps
39. Analysis of Variance
40. Multiple Comparison Tests After ANOVA
41. Nonparametric Methods
42. Sensitivity Specificity and Receiver-Operator Characteristic Curves
43. Sample Size
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

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