Rating: 
Summary: The beginner's viewpoint
Review: Frankly, I am aghast that the publisher calls this book accessible and appropriate for beginners. I purchased it as the required text for an introductory biometry class, and it has contributed very little to my understanding of biostatistical analysis. After chapter 7 (at best), it is simply incomprehensible. The mathematical notation requires far more than a high school algebra background, and the wording is extremely terse. Yes, it does offer real-world examples, and yes, it does offer a large range of statistical tables, and I am sure that it is good reference for people who are already working as statisticians and who have advanced degrees in statistics. However, it is misleading to call Zar a good introductory text - it is completely inappropriate for beginners. It is too concise in a situation where more words would be useful. Sokal and Rohlf 2nd ed. is far better; Fowler et al. also proved helpful.
Rating: 
Summary: The medium?s viewpoint
Review: I had been teacher of Statistic by several years in some universities of Mexico and the Zar book had been a common company in my classroom all the years. Certainly it is not to begginers, but it is very usefull to proffesors. It usually have answer to assess uncommon series data, as non-normal data, or missing data. The book work almost all the time with data series that have normal distributions, and use non-parametric statistical tools to work with other series distributions. There are other books that deal with it best, considering, by example, tests to data with Poisson or binomial distributions (the common in the biological sciences). However, to undergraduate students, and some graduate students, the Zar "normal" approach work fine. Graduate research or doctoral research is done better not with other book in hand but with other statistical approach in mind, and unfortunately there is not only book to do it. Well, in that case, the Zar book help to understand some of the main points to make high statistical analysis.
Rating:
Summary: The beginner's viewpoint
Review: I have found this book more approachable and user friendly than Sokal and Rohlf. Zar is an excellent desk reference, and has solved a number of statistical problems for me. I reccomend it to anybody who regularly uses statistics in a biological context.
Rating: 
Summary: A Great Desk Reference for Biologists
Review: I have found this book more approachable and user friendly than Sokal and Rohlf. Zar is an excellent desk reference, and has solved a number of statistical problems for me. I reccomend it to anybody who regularly uses statistics in a biological context.
Rating:
Summary: Stop being afraid of biostatistics!
Review: The author knows the difficulties found by researchers when they need to analyse their data.
He writes about all topics of statistical analysis in a very simple way, and the solved examples makes our understanding smooth. He knows how to drive us in such a way that analyzing biomedical data becomes compreehensible and easy! A book that biologists and people from the biomedical area must consult everyday! This book should be translated to other languages so much more people could learn from it!
Lucia M. Singer, M.Sc. Ph.D.
Rating:
Summary: Excellent reference text on biostatistical analysis
Review: This book and BIOMETRY by Sokal and Rohlf are the two standard statistics books that live on the shelves of most biological researchers. Zar makes methods and interpretations of statistical analyses accessible and understandable. There are suffient numbers of statistical tests in the text to cover most of my statistical needs. The book also includes its own tables of critical values (something that BIOMETRY does not -- there's a paperback companion book of tables for that book). Zar also includes examples that are easy to follow, as well as enough mathematical background to allow one to understand the hypothetical and much of the mathematics behind the statistical methods. I typically turn to Zar first when I have a statistical question. This book continues to be a highly prized ally of mine in the data analysis game. I highly recommend this book to all biology grad students and researchers. It's well worth the price -- an investment that pays off! 5 stars, no doubt about it.
Rating:
Summary: Excellent reference text on biostatistical analysis
Review: This book and BIOMETRY by Sokal and Rohlf are the two standard statistics books that live on the shelves of most biological researchers. Zar makes methods and interpretations of statistical analyses accessible and understandable. There are suffient numbers of statistical tests in the text to cover most of my statistical needs. The book also includes its own tables of critical values (something that BIOMETRY does not -- there's a paperback companion book of tables for that book). Zar also includes examples that are easy to follow, as well as enough mathematical background to allow one to understand the hypothetical and much of the mathematics behind the statistical methods. I typically turn to Zar first when I have a statistical question. This book continues to be a highly prized ally of mine in the data analysis game. I highly recommend this book to all biology grad students and researchers. It's well worth the price -- an investment that pays off! 5 stars, no doubt about it.
Rating: 
Summary: Concise but not beginner friendly
Review: This book is popular because it is well written and authoritative. It is written for biologists, medical students and researchers who do not have any prior knowledge of probability or statistics and may have little mathematical training as well. It serves as an introductory text providing many homework exercises. It can also be used as a reference. It is very thorough and covers most of the important topics required for biological problems. The needed probability is introduced when necessary. There is the usual emphasis on hypothesis testing and regression. Correlation and analysis of variance are also very well covered. Important issues of sample size determination are covered and many solutions are provided in easy to use box descriptions. As the author points out in the preface, in order to make this text a good reference it is extensive (663 pages of text followed by appendices and a large number of tables). It also includes a wealth of useful reference articles and books. Consequently, there is too much material for a one semester course. The author provides instructors with guidelines for sections to cover in an introductory course. Notable topics covered in this text that is rarely found in introductory biostatistics books include multivariate methods especially the multivariate analysis of variance (MANOVA)and inference for circular data. Recent developments in meta analysis, Bayesian statistics and bootstrap methods are not covered. In fact, these topics are not covered at all. Also, the important topic of missing data is omitted. Outliers are only covered briefly and just a few references are given but the major references, the texts by Hawkins and the treatise of Barnett and Lewis are neglected. I am currently working on an elementary text that will have the advantage of some real world applications and modern developments. There are a few other elementary statistical texts for biology that are worth considering including Motulsky's "Intuitive Biostatistics" and Riffenburgh's "Statistics in Medicine". My favorite is the slightly more advanced "Practical Statistics for Medical Research" by Doug Altman.
Rating:
Summary: one of the most popular introductory biostatistics books
Review: This book is popular because it is well written and authoritative. It is written for biologists, medical students and researchers who do not have any prior knowledge of probability or statistics and may have little mathematical training as well. It serves as an introductory text providing many homework exercises. It can also be used as a reference. It is very thorough and covers most of the important topics required for biological problems. The needed probability is introduced when necessary. There is the usual emphasis on hypothesis testing and regression. Correlation and analysis of variance are also very well covered. Important issues of sample size determination are covered and many solutions are provided in easy to use box descriptions. As the author points out in the preface, in order to make this text a good reference it is extensive (663 pages of text followed by appendices and a large number of tables). It also includes a wealth of useful reference articles and books. Consequently, there is too much material for a one semester course. The author provides instructors with guidelines for sections to cover in an introductory course. Notable topics covered in this text that is rarely found in introductory biostatistics books include multivariate methods especially the multivariate analysis of variance (MANOVA)and inference for circular data. Recent developments in meta analysis, Bayesian statistics and bootstrap methods are not covered. In fact, these topics are not covered at all. Also, the important topic of missing data is omitted. Outliers are only covered briefly and just a few references are given but the major references, the texts by Hawkins and the treatise of Barnett and Lewis are neglected. I am currently working on an elementary text that will have the advantage of some real world applications and modern developments. There are a few other elementary statistical texts for biology that are worth considering including Motulsky's "Intuitive Biostatistics" and Riffenburgh's "Statistics in Medicine". My favorite is the slightly more advanced "Practical Statistics for Medical Research" by Doug Altman.
Rating:
Summary: Concise but not beginner friendly
Review: This book is very concise and very mathemically based. It does a good job at presenting the mathematical models of statistics but it does a lousy job at explaining biostatistics in a conceptual manner. Maybe a great book for those who love mathemical models and notations but definitely not recommended for someone who is trying to understand biostatistics conceptually. If you are a scientist with a weak bacdground in statistics, you may find it a waste of time to try to understand this book.
| 
