Customer Reviews

Intuitive Biostatistics, First Edition

5 star:		(12)
4 star:		(2)
3 star:		(0)
2 star:		(0)
1 star:		(0)

 

 

I'm doing my PhD and needed something to strengthen my basic biostatistics for the comprehensive exams and for my career (of course). I purchased this book because of the great reviews it has.

The strength:
It indeed deserves the five stars IF you are looking for a book to help you lay down a good stats foundation. Its main strengths are the explanation of the very basic concepts such as confidence intervals, P values, standard deviation, survival curves, relative risk, (a little of) odds ratio, clinical/screening tests, etc. To be honest, I only began to really understand and differentiate all these concepts after reading this book. It is very clear, easy to understand, and very repetitive (in a good way). Now I feel so more confident in interpreting research papers (with all those overwhelming P values) and stats outputs.

The weakness:
Motulsky spends a great deal of chapters explaining the basics I mentioned earlier. Therefore, if what you need is something on statistic tests such as ANOVA, MANOVA, regression, etc., this book wouldn't deserve five stars for you. Although it does try to explain how to interpret the output, the F value and alike, it is very, very superficial. In this case you would better off getting a more advanced statistics book.

But it definitely fitted my needs, so it does deserve five stars to me.

A really nifty book for anyone--and that's most of us--interested in what basic statistical tests mean and how to use them. Even someone with a pretty advanced knowledge of statistics may not understand all of the intuitive concepts described in this book. Consistent with its title, it is probably best suited for those in the biosciences, rather than engineers, for example, but I'd say that people in those more technical fields may very well benefit greatly from reading it. It's written in a conversational manner that is easy to digest. I'd say a lot of thinking by the author went into creating it, because he seems to guess what the reader may be thinking and then answers those questions. He's big on the confidence interval, too. Readers who need more specialized, detailed info on a particular topic, such as two-way ANOVA, probably need to consult an additional text. Overall, a great introduction to fundamental statistical concepts and tests, that will be of interest to biological scientists and many other folks, too. Author of Adjust Your Brain: A Practical Theory for Maximizing Mental Health.

I used this book when writing a manuscript for publication. I stumbled across the author's web site, and found his approach remarkably useful. The book is clear and concise. It encompasses all general statistical methods needed for biomedical publishing for both basic and clinical research. It serves as an indespensible introduction for those, who like me, either never understood biostats as taught in medical school or who have not used these skills often enough to develop adequate familiarity with the methods and their application. Highly recommended.

I am very grateful that a professor of mine used this book for her class...I had flipped thru other stats books prior to taking her class, and the contents of those other books looked all too similar to the mathematics books that had tortured me for years.
Intuitive Biostatistics is actually enjoyable to read, and it usually teaches at a pace that is reasonable. Many of the examples were quite relevant to my area of study - Nutrition.
Wish more texts were like this one.

This book was an excellent choice for me to help review biostats for my prelims! Yes, although it doesn't focus on details of the more advanced statistical analyses, it does offer a common sense approach to data analysis. The assumptions for each test are clearly delineated, and the table at the back of the book is a quick reference for choosing tests based on what type of data you have. This is the ONLY biostatistics book I have read cover to cover.

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.

I'm taking a class which uses this book as its primary textbook.

Its good for people who would like a refresher to statistics, or those who have never been exposed to stats.

But I found the book skimpy on some of the more important aspects of Statistics. The book is terse and packed with useful clinical information. I highly recommend this book to any clinical person who is looking to brush up on their stats.

Edit: Updated 9/5/02. I have referred to this book countless times since my class has ended. Some topics that are not covered well in Motulsky's book are: multivariate analysis, ANCOVA, Reliability studies and ANOVA. I installed a demo version of Motulsky's GraphPad software and was very impressed. I had a question so I emailed the tech support and Dr. Motulsky himself responded within an hour!

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.

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.

...to practical statistical methods I've seen. Dr. Motulsky articulately enables consideration and application of statistical methods. Flow is excellent throughout the book, both within and between topics. This is a great reference when using the Graphpad program Prism (also an excellent and equally accessible resource), but it would be an excellent companion to any computer stat program. If you need a handy book, I recommend this one without reservation - it's like a statistical Swiss Army knife.