Intuitive Biostatistics (Paperback)

by Harvey Motulsky (Author) "This is a book for "consumers" of statistics..." (more)
Key Phrases: hypermobile wrists, mean stool output, independent null hypotheses, United States, Analysis of Example, Disease Present Disease Absent Total (more...)

Editorial Reviews

Review

"I like this book much, I will be "beta-testing" it on my upper-level undergraduate biostatistics class. It fills a great need for my students."--Harriette Phelps, University of D.C.
"This splendid book meets a major need in public health, medicine, and biomedical research training--a user-friendly biostatistics text for non-mathematicians."--Gilbert S. Omenn, Executive Vice President for Medical Affairs at the University of Michigan
"Motulsky has written a very readable and delightful account of how statistics are used in biology and medicine...He focuses on clinical studies and covers a broad range of topics, including...such specialized areas as survival analysis, Bayesian inference, and logistic regression." --Quarterly Review of Biology
"The unique aspect of the book, which makes it different from other biostatistics books, is its approach to the content...His goal is to help the reader interpret medical literature rather than analyze a set of data....I higly recommend this book for those needing a non-mathematical, explanatory introduction to biostatistics. It is well-written and provides wonderful clinical examples and biostatistical content...An excellent resource book for medical students and housestaff who are struggling along with the concepts; and for those of you who were wondering, it was surprisingly easy to read."--Joseph Chu, MD, MPH, University of Washington in Teaching and Learning Medicine


Product Description
Designed to provide a nonmathematical introduction to biostatistics for medical and health science students, graduate students in the biological sciences, physicians, and researchers, this text explains statistical principles in non-technical language and focuses on explaining the proper scientific interpretation of statistical tests rather than on the mathematical logic of the tests themselves.
Intuitive Biostatistics covers all the topics typically found in an introductory statistics text, but with the emphasis on confidence intervals rather than P values, making it easier for students to understand both. Additionally, it introduces a broad range of topics left out of most other introductory texts but used frequently in biomedical publications, including survival curves. multiple comparisons, sensitivity and specificity of lab tests, Bayesian thinking, lod scores, and logistic, proportional hazards and nonlinear regression.
By emphasizing interpretation rather than calculation, this text provides a clear and virtually painless introduction to statistical principles for those students who will need to use statistics constantly in their work. In addition, its practical approach enables readers to understand the statistical results published in biological and medical journals.

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Product Details

• Paperback: 408 pages
• Publisher: Oxford University Press, USA; 1 edition (October 19, 1995)
• Language: English
• ISBN-10: 0195086074
• ISBN-13: 978-0195086072
• Product Dimensions: 9 x 6.1 x 0.9 inches
• Shipping Weight: 8 ounces (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars See all reviews (28 customer reviews)
• Amazon.com Sales Rank: #26,297 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

63 of 64 people found the following review helpful:

4.0 out of 5 stars book lives up to its title, September 7, 2000

By	Michael R. Chernick "statman13" (Malvern, PA) - See all my reviews (REAL NAME)

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.

30 of 30 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)

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.

26 of 26 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

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.