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Intuitive Biostatistics: a Nonmathematical Guide to Statistical Thinking, 2nd Revised Edition 2nd Revised & enlarged Edition

4.5 out of 5 stars 31 customer reviews
ISBN-13: 978-0199730063
ISBN-10: 0199730067
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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 FOR 2nd EDITION (3rd NOW AVAILABLE)
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
 
 
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Product Details

  • Paperback: 508 pages
  • Publisher: Oxford University Press; 2nd Revised & enlarged edition (January 20, 2010)
  • Language: English
  • ISBN-10: 0199730067
  • ISBN-13: 978-0199730063
  • Product Dimensions: 9.2 x 1 x 6.2 inches
  • Shipping Weight: 1.4 pounds
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (31 customer reviews)
  • Amazon Best Sellers Rank: #373,171 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

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I'm a retired cancer researcher (molecular biology) turned biomedical writer/editor. My use of statistics (and increasing ignorance thereof) was limited, because, in general, if an experiment didn't yield at least a 10x difference, we threw it out. Now I can't do my job without knowing biostatistics. Moltulsky's book has been enormously helpful in teaching me the basics of biostatistics and enabling me to evaluate manuscripts for correct use of statistical terms and methods. Previously I used (and still do) Motulsky's manual for his GraphPad software, the AMA Manual online, and, of course, the internet. The only problem with the book is that a digital version isn't available. However, as the author pointed out to me, you can search the book to some extent on its Amazon.com product page. Finally, the author's immediately available via email. This is a terrific resource and value!
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I studied statistics nearly all my life, and even then, this book was an enlightenment to me. I would recommend it not only to biostatistocians, but also for other fields where statistics is heavily used.

I bought this book as a recommendation from my supervisor, who owns a previous edition. But after I started reading it, and tell him the concepts, and why our data looked like it did, and why we did not get significant results or why we did get it, and whats a true meaning of t-statistics, and the theory behind confidence intervals, we decided to compare the versions, and we realized that first edition was way more formal and mathematical. So now he ordered this new addition too.

There are nearly no formulas in the book, it is very easy to read from head to tail. It's almost like reading a novel!

I am already half way through and I am very looking forward for the next chapter, which in fact never happened to me when reading a scientific material. Also, Q&A section is very nice, where you can check your understanding of concepts, or just see the correct answers to tricky questions.
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Great book for the non- researcher. Perfect for doctors who need to analyze the literature. Very intuitive approach, little math skills needed, but includes equations when helpful and for those interested in the math.
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By Bitz on September 8, 2011
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This is a wonderful book that clearly and creatively explains some difficult epidemiologic and biostatistical concepts. As a practicing epidemiologist I find this book an invaluable reference. A must have for any graduate student in public health.
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My stats background is weak, and I have only so much time available to strengthen it. As my research develops, I need to be able to talk with statisticians and this book is helping my conceptual understanding. I keep going back to it for individual topics and plan to read it cover-to-cover.
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This book covers all of the essential biostatistics topics that I've ever encountered (I'm a PhD student studying environmental health). What is unique about this book, and incredibly helpful, is that it describes statistical tests and concepts using WORDS, not formulas, numbers, or proofs. This is incredibly rare when it comes to statistics books.

I would highly recommend this book to anyone who, after consulting a standard biostatistics textbook, is left saying "huh?"
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Sure, the title of the book is 'Intuitive *bio*statistics'. Maybe only a medical researcher is exposed to sufficiently many variations of statistical problems to write such a book. The 'bio' part only applies to the case examples. While a layman will not be able to grasp the details of the biological or medical questions discussed in the examples, anyone will be able to understand the statistical reasoning and the trade-offs and choises made.

Anyone working with statistics will benefit from reading this book.

As for rating, I could have given it four stars in anticipation of a future 3rd edition that might have addressed the bio/med layman rather than the biotech/medicine professional. But this book is out there now. 5 stars, no question.
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This biostatistics book is very well written with lots of examples from real experimental data. It is easy to follow and one can always go back to it as a reference when in doubt of what statistical method to use for a planned experiment. The book is very light on math. I did miss a few more equations, however that may even be a plus for people with lesser mathematical background.
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