Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking [Paperback]

Harvey Motulsky (Author)

Book Description

Publication Date: January 20, 2010 | ISBN-10: 0199730067 | ISBN-13: 978-0199730063 | Edition: 2nd Revised & enlarged

Overview

Intuitive Biostatistics is both an introduction and review of statistics. Compared to other books, it has:

• Breadth rather than depth. It is a guidebook, not a cookbook.
• Words rather than math. It has few equations.
• Explanations rather than recipes. This book presents few details of statistical methods and only a few tables required to complete the calculations.

Who is it for?

I wrote Intuitive Biostatistics for three audiences:

• Medical (and other) professionals who want to understand  the statistical portions of journals they read. These readers don't need to analyze any data, but need to understand analyses published by others.
• Undergraduate and graduate students, post-docs and researchers who will analyze data. This book explains general principles of data analysis, but it won't teach you how to do statistical calculations or how to use any particular statistical program. 
• Scientists who consult with statisticians. Statistics often seems like a foreign language, and this text can serve as a phrase book to bridge the gap  between scientists and statisticians.

What's new in the second edition?

Though the spirit of the first edition remains, very few of its words do. It is hard to explain what is new in this edition, since I essentially rewrote the entire book. New and expanded topics in the second edition of Intuitive Biostatistics include:

• Chapter 1 explains how our intuitions can lead us astray in issues of probability and statistics.
• Chapter 11 (and later examples) highlight the fact that lognormal distributions are common.
• Chapter 21 explains the idea of testing for equivalence vs. testing for differences. 
• Chapters 22, 23, and 40 discuss the pervasive problem of multiple comparisons. 
• Chapters 24 and 25 discuss testing for normality and for outliers.
• Chapter 35 shows how to think about statistical hypothesis testing as comparing the fits of alternative models.
• Chapters 37 and 38 give expanded coverage of the usefulness--and traps--of multiple, logistic, and proportional hazards regression.
• Chapter 43 briefly mentions adaptive study designs where sample size is not chosen in advance.
• Chapter 46 (inspired by, and written with, Bill Greco) reviews many topics in this book and more general issues of how to approach data analysis.

Editorial Reviews

Review

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 (added by author)


"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

From the Author

View the web page for this book, including errata, at intuitivebiostatistics.com

CONTENTS

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

 

 

See all Editorial Reviews

Product Details

• Paperback: 512 pages
• Publisher: Oxford University Press, USA; 2nd Revised & enlarged edition (January 20, 2010)
• Language: English
• ISBN-10: 0199730067
• ISBN-13: 978-0199730063
• Product Dimensions: 9.2 x 6.1 x 0.9 inches
• Shipping Weight: 1.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars  See all reviews (43 customer reviews)
• Amazon Best Sellers Rank: #8,012 in Books (See Top 100 in Books)
  • #1 in Books > Medical Books > Basic Sciences > Biostatistics
  • #53 in Books > Professional & Technical > Professional Science > Biological Sciences > Biology

More About the Author

After graduating medical school and doing an internship in internal medicine, I switched to research in receptor pharmacology research (and published over 50 peer reviewed articles). While I was on the faculty in 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 Intuitive Biostatistics.

I hated creating graphs by hand, so I created some programs to do so. I also created some simple statistics programs when I saw that the existing statistical software, while great for statisticians, was overkill for most scientists. These efforts became the beginnings of GraphPad Software, Inc. (www.graphpad.com), which has been a full-time endeavor for me for many years.

Customer Reviews

Most Helpful Customer Reviews

42 of 42 people found the following review helpful:

5.0 out of 5 stars excellent elementary book on biostatistics, February 9, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Intuitive Biostatistics, First Edition (Paperback)

Dr. Motulsky is an MD who is also a Professor of Pharmacology and President of his own software company. The book's title suggests that he can make biostatistics intuitive for non-statisticians (e.g. physicians, clinicians and nurses). After reading through it he has made a believer out of me! He introduces concepts through examples and touches on most of the important statistical methods that are used in the medical literature. While the book could be used as a classroom text, it seems to me to be more suited as a reference source for medical researchers who want to understand the statistics described in research papers. Although not a statistician by training, Dr. Motulsky has a good understanding of statistical methods and principles and exhibits his wisdom and experience throughout the book. He is deliberate at keeping things simple and to the point. He points out that he intentionally uses fake examples and modifies real examples for simplification of exposition. He avoids mathematics as much as possible. the preface and the introduction are very well written and the reader should read both before reading the rest of the text.
My usual concern with such books is that concepts are oversimplified and the presentation is too cook-bookish. Amazingly that is not the case here. Professor Motulsky carefully explains concepts such as confidence intervals, p-values, multiple comparison issues, Bayesian thinking and Bayesian controversy in a way that should be understandable to his intended audience.

Proportions and the binomial distribution are introduced early. Advanced topics such as sequential methods, survival curves and logistic regression are tackled. These subjects are important in medical research but are often avoided in elementary books. To his credit he also does a very good job of introducing the concepts of sensitivity and specificity. Hypothesis testing is introduced at the same time which makes a lot of sense since for a particularly hypothesis test the specificity and the sensitivity are related to the type I and type II errors. It is a good way for those familiar with medical applications where specificity and sensitivity may be intuitive concepts, to become comfortable with the less familiar null and alternative hypotheses and their associated error probabilities.

Professor Motulsky writes eloquently and this appears to be appreciated by the readers, judging from the other reviews that I have seen on Amazon. Having said all this you might wonder why I didn't give it 5 stars. I found a few things that could have been done better.

I am not completely happy with the way probability is introduced through the binomial distribution and here the wording could be improved. He writes "Mathematicians have developed equations, known as the binomial distribution, to calculate the likelihood of observing any particular outcome when you know the proportion in the overall population." Actually the binomial distribution is a probability distribution (which he has not yet defined as he first uses the term distribution). The equation is a statement that the probability of an event (e.g. exact 7 heads in 10 coin flips) is given by equation (2.2) on page 19 with N=10 and R=7 and p=1/2 (assuming a fair coin).

Another area that could be omitted or else improved is the discussion of Bayesian ideas. Bayes theorem is presented in a limited context related to the example of sensitivity and specificity. While I do think that some Bayesian ideas are well brought out the breadth of applications is missing. Some comparison of the frequentist and Bayesian approaches and philosophy are correctly described but the discussion is too brief to provide good insight. The p-value is strictly a frequentist concept. Motulsky relates it to the Bayesian idea of posterior odds for the null hypothesis to be true. While there is such a formal mathematical relationship, they are conceptually quite different. This is just like relating likelihood to posterior probability. Mathematically the likelihood and posterior probability are related through Bayes theorem as posterior = likelihood x prior but although likelihood is an acceptible frequentist concept posterior probability is not. A real understanding requires some knowledge of the sample space for a frequentist and the treatment of parameters as random quantities by Bayesians. I think this may be something that requires a little more mathematical sophistication than is intended for this readership.

There are a few topics that get little or no treatment but deserve more in a biostatistics texts. These include missing data, resampling methods, hierarchical Bayesian models and longitudinal - repeated measures data. Perhaps we will see intuitive descriptions of some of these topics in the second edition.

29 of 29 people found the following review helpful:

5.0 out of 5 stars This is a great book, September 24, 2000

By 

Joseph Marino (Carmichael, CA United States) - See all my reviews

This review is from: Intuitive Biostatistics, First Edition (Paperback)

I'm a practicing physician who has found it necessary to try to educate myself on the use of biostatistics in the medical literature. I have read over 20 books on biostatistics. This is clearly the best. It is written so that even the non-statistician can understand the concepts, and explains the statistical approach and rationale without scaring the reader away with arcane formulas. It is very logical in its progression and addresses the errors and assumptions that doctors make when trying to evaluate a paper. This book should be required reading not only by every medical student, but by anyone who attempts to write or interpret the medical literature.

25 of 26 people found the following review helpful:

5.0 out of 5 stars Excellent non-mathematical overview, July 4, 2002

By 

Jim Carson "http://www.jimcarson.com" (Bellevue, WA) - See all my reviews
(REAL NAME)   

This review is from: Intuitive Biostatistics, First Edition (Paperback)

Dr. Motulsky does an excellent job of introducing statistical concepts through examples and direct applications. Where this book is especially valuable is in keeping things simple -- without the intimidating mathematical notation -- while providing examples of where statistics can be used to measure the wrong things or present results that do not make sense in the context of what the researcher is investigating.

My favorite example illustrates how a stastical analysis of a new test that identifies those susceptible to a fatal disease "shows" an increase in the average lifespan of both populations (those who suffer the disease and those who don't). The reality, of course, is no one is living longer because of the test, but rather the population sampled is different. Brilliant and concise.

Although the text is targeted towards those in the bioinformatic and medical vocations, it's useful beyond that because the presentation of concepts is practical and yet without the notation.