Customer Reviews

Biometry: The Principles and Practices of Statistics in Biological Research

5 star:		(12)
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This book and BIOSTATISTICAL ANALYSIS by Jerrold Zar are my main "go to" books when I have questions about statistical methods, applications, or interpretation.

Zar's book is probably the more understandable primer text on statistics of the two, but BIOMETRY is better at addressing non-parametrics, though it is certainly not a comprehensive treatment of that field of statistical analysis.

I typically go to Zar first, then to Sokal and Rohlf -- a great one-two combination that takes care of most of my statistical needs.

I appreciate the inside covers of BIOMETRY, with its summary table that provides a starting place for choosing the most likely statistical tests for a give comination of numbers of samples and numbers of variables in an experiment.

You should be advised that the book BIOMETRY does not contain tables of critical values. You will need to purchase the book STATISTICAL TABLES by Rohlf and Sokal to get them.

All in all, an excellent book on statistical methods.

4.5 to 5 stars...I'll give it 5 stars.

Alan Holyoak

"Biometry" should be regarded - if it isn't already - as the standard practical guide to both parametric and nonparametric univariate biostatistics. Although it is aimed primarily for researchers and students in the biological sciences and medicine, there are ample practical examples which should be useful to those in psychology, sociology and other fields where knowledge of statistics and statistical methods is quite essential. I own a copy of the second edition, which I've had for more than two decades, regarding it as my "go-to" book if I have questions about various univariate statistical techniques. As another reviewer has noted, this book is especially strong in discussing nonparametric statistics. I might add too that it is quite good in discussing tests of significance related to Analysis of Variance (ANOVA) and simple linear regression. Much to their credit, the authors have written a superb text in clear, often concise, English that could be used as a self-teaching introductory guide for a practical understanding of univariate biostatistics. However, I will note that those interested in learning the theoretical foundations of the parametric and nonparametric univariate biostatistics covered in "Biometry" should consult another book which does emphasis these topics: Zarr's "Biostatistical Analysis", which I agree is a superb introductory text on theoretical biostatistics.

I have previously reviewed this book. My review pertains to the second edition as that is the only edition I have.
Recently I did some consulting for a colleague. He had some data that he wanted to test for the presence of a single outlier. I referred him to the procedures due to Grubbs and Dixon. I also mention the book by Barnett and Lewis which has the most detailed account of outlier methods. However, Barnett and Lewis is so detailed that it can be overwhelming for a beginner. Fortunately my friend has a copy of Sokal and Rohlf's book. I believe he has the same second edition that I have. They provide a good elementary treatment of these methods and have tables to use. Unfortunately, I discovered that the tables are in a separate supplement. My colleague has the supplement but I don't. The reader should be aware that the supplement is needed to implement some of the procedures in the book that require tables. It is not expensive but it is essential. I imagine that the same is true for the third edition but I am not sure.

This book is commonly used in the medical device/ pharmaceutical clinical trials industry. A colleague of mine who I worked with in medical devices used this text as one of his primary statistical reference books. He is not a statistician but rather a regulatory affairs specialist.

I took Biometry as a course at Stony Brook with Rohlf. I think the book does an excellent job at building a conceptual understanding of what different statistical techniques are used for. Coming out of the course (which was essentially Rohlf reading his book to us), one develops an excellent ability to look at an experiment and determine what type of analysis should be done. This is pretty damn important. What the book fails to do is, once you know which test to use, help you analyze your data using popular statistical programs. Other texts (e.g., Tabachnik and Fidel) appreciate that knowing which test to use is only half the battle. Biometry is weak at helping you actually run tests.

One great stength of Biometry is its treatment of non-parametric data. It is by far the best treatment I have seen in an introductory text. I would highly recommend this book to anyone whose data violates assumptions of the typical ANOVA model.

Although many students and educators now learn about statistics by using plug-n-play statistics packages like SAS or SPSS, a basic understanding of even the most fundamental statistical principles is essential to sound statistical reasoning. _Biometry_ is a wonderful introduction to statistics. Although designed for ecologists and evolutionary biologists, and although most of the examples are from the life sciences, it is an excellent introduction to statistical reasoning for anyone in any field. I would highly recommend this book, a title on Exploratory Data Analysis, and hands-on experience with a statistical package as the ideal introduction to statistics.

Biometry is a required course so I had to buy the book. It's readable, and on amazon its cheaper than at the university book store. I would recommend this book if you have no choice and have to take the course.

Currently, there are many statistical software packages available for researchers to perform statistical tests. Thus many statistical books become less useful than in the past.

However, this is not the case for this book. This is so because this book contains many topics which are currently ignored in statistical software packages, for example, model II ANOVA, which gave me the tool to analyze many data producing about ten papers in research journals and books.

Another example would be the Jackknife test, which is not easy to find in other statistical textbooks.

Finally, this book does have many worked examples, which can teach me hand in hand how to calculate. Actually not many books can be written in such a good way.

I use this as a personal reference. Have examined many dozens of statistics books and this is the Bible, as far as I am concerned. It is clear, has excellent coverage, and is actually written engagingly! If you are going to have only one statistics book--it should be this one.

I used this book for a college-level statistics course and thought it was great. The text can be kind of dry (what textbooks aren't?), but the examples in each chapter to highlight a statistical method really help solidify the concepts. The questions at the end of each chapter are also really good. I just wish I had time to do them!

If you are learning or need to understand statistics, this is the ultimate book for you. It gives you a deep insight into the complexity of statistics in biology.