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Title: Intuitive Biostatistics
ISBN: 0195086074
Author:
Harvey Motulsky
Publicate Date: 1995-10-19
Publish: 1995-10-19
List Price: $49.95
Average Customer Rating: 4.5
Format: Paperback
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Amazon Lowest New Price: $43.37
Amazon Lowest Used Price: $33.00
Amazon Merchant Price: $43.45
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| Customer Review: |
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1: Intuitive Biostatistics and Me
This is an excellent addition to my personal, work library of biostatistics and statistics textbooks. I purchased it specifically since it has a formula for calculating confidence intervals for ratios. This formula was not in my favorite statistics textbook, "Statistical Methods" by Snedecor and Cochran. I highly recommend "Intuitive Biostatistics" to other routine users of statistical methods in the field of biology.
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2: A fantastic resource
This text is by far the most readable book on statistics I've ever read. In addition, the software written by this author (GraphPad Prism) is also the most user-friendly and intuitive package available. In my opinion, the major benefit of this book is that it gets the reader to understand the conceptual basis of various experimental designs and statistical analyses, rather than blindly dumping data into a statistical package and hitting "go".
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3: Invaluable stats handbook for nonmathematicians
One of the best handbooks I have ever seen in any subject. Since statistics or generaly mathematics is pretty hard for biologists to lern, it require special teching aproach designed to demonstrate logics behind statistical concepts. This book is uniquely doing exectly that. I have used several books in statistics for biologists, including small intorductory material and heavyweight Biometry, as well as numerous online stat dedicated sites. This is the book I strongly recommend for the bigginers interested in lerning statistics.
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4: Excellent book
This book goes straight to the point, assisting you in making the proper decisions with the statistical tests you need to use. Well written, well organized. A really good book coming from the same person who brought us a really good software (Prism).
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5: excellent elementary book on biostatistics
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.
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