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Showing 1-10 of 26 reviews(Verified Purchases). See all 33 reviews
on July 17, 2012
I'm a retired cancer researcher (molecular biology) turned biomedical writer/editor. My use of statistics (and increasing ignorance thereof) was limited, because, in general, if an experiment didn't yield at least a 10x difference, we threw it out. Now I can't do my job without knowing biostatistics. Moltulsky's book has been enormously helpful in teaching me the basics of biostatistics and enabling me to evaluate manuscripts for correct use of statistical terms and methods. Previously I used (and still do) Motulsky's manual for his GraphPad software, the AMA Manual online, and, of course, the internet. The only problem with the book is that a digital version isn't available. However, as the author pointed out to me, you can search the book to some extent on its product page. Finally, the author's immediately available via email. This is a terrific resource and value!
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on January 13, 2010
This is a first-rate book on biostatistics. Strongly recommended for physicians and others who read and must make sense of the biomedical literature. It should also be required reading for scientists who are publishing biomedical research, preferably early in their careers when they are still impressionable. Clearly shows the strengths and limitations of statistics in interpreting research results. Does a great job on the ever-present problem of multiple comparisons and retrospective data torture. Other texts are cookbooks which show you how to perform various statistical tests, but ultimately it is far more important to understand the rationale behind statistical methodology and to see what statistical tests can--and can't--tell us. Enforces the notion that statistical tests have assumptions and describes when these assumptions can be violated and when such violations render the tests worthless. Debunks the notion that "statistical significance" is an absolute concept and shows how p<0.05 is a convention, and an arbitrary one at that. Nice section on the weakness of correlation coefficients for data which violate the assumptions of that test. Strong on applying Bayesian thinking to marginal p values. Points out the important distinction between clinical and statistical significance. All in all, a great book.
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on October 29, 2014
Possibly one of the best scientific textbooks I've ever read; it really manages to deliver many important concept of biostats in a very reader-friendly format, without requiring fancy math knowledge.
There are a quite a few errors here an there, but there's a good errata online to account for them, and also a new edition has come out since I've bought it.

In my opinion, this should be read by any biology PhD or MD.

Very highly recommended
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on February 14, 2013
This biostatistics book is very well written with lots of examples from real experimental data. It is easy to follow and one can always go back to it as a reference when in doubt of what statistical method to use for a planned experiment. The book is very light on math. I did miss a few more equations, however that may even be a plus for people with lesser mathematical background.
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on December 2, 2013
This book presents most of the common statistical methods in a conceptual way and gives many examples from actual biomedical studies. It greatly emphasizes when a statistical method should or should not be used, and corrects some the most common misconceptions in statistics.

While the book contains some math and does quite a good job explaining it to the non-mathematician, this may not be the best book for someone who wants to deeply understand statistics from a mathematical point of view (and the author acknowledges that).
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on March 4, 2010
This is a must for every medical student. For those engaging in research there is sufficient detail to be able to apply the correct statistical technique and to interpret the results. For the rest it gives a very clear introduction to biostatistics and particularly aids understanding of research articles without which a doctor will quickly lose touch with evidence based medicine.

For the scientist and science student I would recommend this as an adjunct to any statistical course and as a reference to have on hand when reading those articles. I regularly picked up the first edition to check I was understanding something correctly - this edition is an order of magnitude better.
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on April 5, 2011
I have read numerous statistics books, most of which are very poorly written. This book, however, is very well written and has many examples of interest to those who work in the medical field. This should be required reading for all biomedical researchers and physicians. For those who have a fear of statistics this book will help you overcome your fears. The author should be applauded for pointing out numerous misconception with regards to statistics and clear explanations for which statistical analysis should be used and how the results should be properly interpreted.
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on October 30, 2013
I am a scientist and have a side interest in statistics -- after all statistics quantifies randomness, error and helps us interpret meaning from data. This should be required reading for all scientists. If so,the quality of science may improve dramatically. I love the most common application errors are identified and put in context. Although, my lab members would probably prefer that I let them continue making the same old mistakes...
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on September 2, 2011
This book covers all of the essential biostatistics topics that I've ever encountered (I'm a PhD student studying environmental health). What is unique about this book, and incredibly helpful, is that it describes statistical tests and concepts using WORDS, not formulas, numbers, or proofs. This is incredibly rare when it comes to statistics books.

I would highly recommend this book to anyone who, after consulting a standard biostatistics textbook, is left saying "huh?"
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on September 16, 2012
This statistics book is very readable and understandable, targeting scientists in biological sciences, medical doctors etc... The goal is to provide them with a broad understanding what are typical problems and methods in statistics and to enable them to critically interpret statistical results that someone else has calculated. The book is full of relevant examples, good illustrations and well-structured insights. Highly recommended!
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