Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking |  | Author: Harvey Motulsky
Publisher: Oxford University Press, USA
Category: Book
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Rating:  34 reviews
Sales Rank: 17376
Media: Paperback
Edition: 2 Revised & enlarged
Pages: 512
Number Of Items: 1
Shipping Weight (lbs): 1.5
Dimensions (in): 9.2 x 6.1 x 0.8
ISBN: 0199730067
Dewey Decimal Number: 610.721
EAN: 9780199730063
ASIN: 0199730067
Publication Date: January 20, 2010
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Product Description
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.
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| Customer Reviews:
Showing reviews 1-5 of 34
 excellent elementary book on biostatistics February 9, 2008
Michael R. Chernick (Holland PA)
37 out of 37 found this review helpful
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.
This is a great book September 25, 2000
Joseph Marino (Carmichael, CA United States)
28 out of 28 found this review helpful
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.
Excellent non-mathematical overview July 5, 2002
Jim Carson (Bellevue, WA)
25 out of 26 found this review helpful
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.
Outstanding Biostatistics Book for the Non-statistician February 1, 2000
Donald Brand (New York City)
18 out of 18 found this review helpful
I was delighted to discover this book four years ago, when I began teaching a course in clinical research methods. I have not seen anything else like the text or the optional companion software by GraphPad. The textbook is extremely well-written and well-organized. Not only have I been using the book and software in my teaching, but I have found that they handle about 90% of my own statistical needs for my research.The author's philosophy (to forget the fancy stuff since the vast majority of applications don't require it) and the way it is implemented (with just enough theory to protect users from making false inferences) are exactly right. Dr. Motulsky has done a a superb job.
A great non-technical introduction to biostatistics November 2, 2004
M. Garrison (Seattle, WA USA)
18 out of 18 found this review helpful
I often recommend this book to two different groups:
* colleagues who want to have a better understanding of the factors that drive statistical methods in medical research, without having to learn the actual statistics themselves
and
* students who are soon going to be taking biostatistics for the first time, and are anxious about whether they will be able to understand the material. For those who are a little on the math phobic side of things, this can be a great introduction to read through before formal coursework begins.
Showing reviews 1-5 of 34
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