Customer Reviews

Intuitive Biostatistics, First Edition

5 star:		(35)
4 star:		(5)
3 star:		(0)
2 star:		(3)
1 star:		(0)

 

 

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.

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 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 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.

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.

I adopted this book as the text for a one quarter course in introductory biostatistics at UCSD Extension. I like the "spirit" of the book, and feel that it meets the needs of biomedical professionals who are our audience, better than a standard introductory statistics text such as Triola or Freund & Wilson. The stress in the book is placed on conceptual understanding of confidence intervals instead of mechanical computation of p-values.
As a mathematician, however, I was disappointed by the lack of rigor in the book, and especially at the plethora of mistakes, both in the text and in the solutions to the exercises. So one must teach from this book with caution, and use this book with a supplement, such as Schaum's Outline or Cliff's Notes, if one wants students to learn how to do the statistics.

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 am not a high faltuin' math person, the calculus I went through in undergrad was a struggle and I remember very little. I am a chemist by training, currently seeking my PhD in Public Health while working full time. What that came down to was little to no time to doof around with a muddled textbook or an equally muddled professor or a non-English speaking Teacher's Assistant.

I have no intention of becoming a biostatistician or an epidemiologist, I am interested in policy.

So coming from that perspective, as a student, this book was an absolute God-send.

Not only did I get an A in the class, but I feel like I have a sturdy foundation for my future coursework and career. I will not be intimidated by numbers or jargon because Dr. Motulsky made it all as straightforward and clear as possible, and I recall even laughing a few times.

Overall, if you are in school, facing a biostatics class with extreme trepidation, buy this book as a supplement. Look up the topics in the index as you go and you will have more than the $40 worth of "eureka" moments.

The majority of reviewers really liked this book. I can see why, I did too. The author uses a unique approach to teaching statistics that is focused on calculating and explaining Confidence Intervals (the minimum and maximum value you expect an outcome to be given a confidence level typically 95%) rather than P values (probability outcome is due to chance). He also uses common sense and clearly distinguishes between what is statistically significant and what is "significant." Thus, he translates well statistical mumbo jumbo into plain English. He tells you what you should care about and look for.

He shares with you all the statistical flaws that clinical studies may have including testing multiple hypothesis to come up with just a single statistically meaningful one, using large samples to prove something trivial, using small samples that raises uncertainty level, etc...

His section on Bayesian Logic is excellent. His table on what test or methodology to use given the shape of the data and objective you have is worth the price of the book alone. That's one of the clearest taxonomy of statistical methods I have seen anywhere.

Some knowledgeable reviewers have picked up a few errors the author made. I stumbled upon a couple while attempting to replicate the calculation of a few examples. I emailed the author and each time within an hour he either clarified the calculation or corrected the typo that was present in the book. Given his prompt answers, I can't ding him for the couple of typos I caught.

Although the author presents this book as an introductory one, I recommend the reader acquires a good foundation in basic statistics before studying this book. Forgotten Statistics would fit that bill. Indeed, `Intuitive Biostatistics' covers a huge amount of ground. It is far more than an introductory text. It covers material that is pretty advanced including nonparametric hypothesis tests, non linear regression, logistic regression, Bayesian analysis, etc... If it is the first time you come across that stuff you'd be well served having a solid stats foundation. Given that, this book has a lot to offer. I'll keep it as a great reference for years.

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.