Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition - Softcover

Motulsky, Harvey

Softcover

ISBN 10: 0199946647 ISBN 13: 9780199946648

Publisher: Oxford University Press, 2013

Synopsis

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.

CONTENTS

Part A: Introducing Statistics 

 1. Statistics and Probability Are Not Intuitive

 2. The Complexities of Probability

 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. The Ubiquity of Multiple Comparison

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 and 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 

48. Answers to Review Problems 

 

About this title

About the Author

After graduating from medical school and doing an internship in internal medicine, I switched to research in receptor pharmacology (and published over 50 peer reviewed articles). While I was on the faculty of the Department of Pharmacology at the University of California San Diego, I was given the job of teaching statistics to first year medical students and to graduate students. The syllabus for those courses grew into the first edition of this book. I hated creating graphs by hand, so I created some programs to do so. I also created some simple statistics programs after realizing that the existing statistical software, while great for statisticians, was overkill for most scientists. These efforts were the origins of GraphPad Software Inc., which has been a full-time endeavor for many years. In this role I email with students and scientists almost daily, making me acutely aware of the many ways that statistical concepts can be confusing or misunderstood.

From the Back Cover

Written for both students and scientists, Intuitive Biostatistics is a non-mathematical
guide to statistical thinking

"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. 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

Intuitive Biostatistics:

• Focuses on how to interpret statistical results rather than on how to analyze data
• Includes very few equations
• Reviews the assumptions behind statistical tests
• Helps readers avoid common mistakes in data analysis
• Extensively discusses the challenges of multiple comparisons
• Is written for both scientists and students

New to the thoroughly revised and updated third edition:

• A new chapter on the complexities of probability
• A new chapter on meta-analysis
• A completely rewritten chapter on statistical traps to avoid
• More sections on common mistakes in data analysis
• More Q&A sections
• New topics and examples
• New learning tools (each chapter ends with a summary and a list of statistical terms)

From the Inside Flap

Excerpt from "Q and A about confidence intervals of "proportions (chapter 4, page 40).

Q. Which is wider, a 95% CI or a 99% CI?
A. To be more certain that an interval contains the true population value, you must generate a wider interval. A 99% CI is wider than a 95% CI. See Figure 4.2.

Q. Is it possible to generate a 100% CI?
A. A 100% CI would have to include every possible value, so it would always extend from 0.0 to 100.0% and not be the least bit useful.

Q. How do CIs change if you increase the sample size?
A. The width of the CI is approximately proportional to the reciprocal of the square root of the sample size. So if you increase the sample size by a factor of four, you can expect to cut the length of the CI in half.

Q. Can you compute a confidence interval of a proportion if you know the proportion but not the sample size?
A. No. The width of the confidence interval depends on the sample size.

Q. Why isn't the CI symmetrical around the observed proportion?
A. Because a proportion cannot go below 0.0 or above 1.0, the CI will be lopsided when the sample proportion is far from 0.50 or the sample size is small.

Q. You expect the population proportion to be outside your 95% CI in 5% of samples. Will you know when this happens?
A. No. You don't know the true value of the population proportion (except when doing simulations), so you won't know if it lies within your CI or not.

Excerpt from "Common mistakes: P values" (chapter 15, page 134)

The P value is not the probability that the result was due to sampling error. The P value is computed assuming the null hypothesis is true. In other words, the P value is computed based on the assumption that the difference was due to randomness in selecting subjects--that is, to sampling error. Therefore, the P value cannot tell you the probability that the result is due to sampling error.

The P value is not the probability that the null hypothesis is true. The P value is computed assuming that the null hypothesis is true, so it cannot be the probability that it is true.

The probability that the results will hold up when the experiment is repeated is not (1.0 minus the P value). If the P value is 0.03, it is tempting to think that this means there is a 97% chance of getting similar results in a repeated experiment. Not so. The P value does not itself quantify reproducibility.

A high P value does not prove that the null hypothesis is true. A high P value means that if the null hypothesis were true, it would not be surprising to observe the treatment effect seen in a particular experiment. But that does not prove that the null hypothesis is true. It just says that the data are consistent with the null hypothesis.

Excerpt from "An analogy to understand power" (chapter 20, page 170)

Here is a silly analogy helps illustrate the concept of statistical power (Hartung, 2005). You send your child into the basement to find a tool. He comes back and says, "It isn't there." What do you conclude? Is the tool there or not? There is no way to be sure, so the answer must be a probability. The question you really want to answer is, What is the probability that the tool is in the basement? But that question can't really be answered without knowing the prior probability and using Bayesian thinking (see Chapter 18). Instead, let's ask a different question: If the tool really is in the basement, what is the chance your child would have found it? The answer, of course, is: it depends. To estimate the probability, you'd want to know three things:

• How long did he spend looking? If he looked for a long time, he is more likely to have found the tool than if he looked for a short time. The time spent looking for the tool is analogous to sample size. An experiment with a large sample size has high power to find an effect, while an experiment with a small sample size has less power.
• How big is the tool? It is easier to find a snow shovel than the tiny screwdriver used to fix eyeglasses. The size of the tool is analogous to the size of the effect you are looking for. An experiment has more power to find a big effect than a small one.
• How messy is the basement? If the basement is a real mess, he was less likely to find the tool than if it is carefully organized. The messiness is analogous to experimental scatter. An experiment has more power when the data are very tight (little variation), and less power when the data are very scattered.

Edition details

• Publisher: Oxford University Press
• Publication date: 2013
• Language: English
• ISBN 10: 0199946647
• ISBN 13: 9780199946648
• Binding: Paperback
• Edition number: 3
• Number of pages: 576