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Book Description
The latest edition of this bestselling biostatistics book is both comprehensive and easy to read. It provides a broad and practical overview of the statistical analysis methods used by researchers to collect, summarize, analyze, and draw conclusions from biological research data. The Fourth Edition can serve as either an introduction to the discipline for beginning students or a comprehensive procedural reference for today's practitioners.
The publisher, PrenticeHall Engineering/Science/Mathematics
This longawaited revision of this bestselling biostatistics text is both comprehensive and easy to read. Provides a broad and practical overview of the statistical analysis methods used by researchers to collect, summarize, analyze, and draw conclusions from biological research data. This text refers to an out of print or unavailable edition of this title.
From the Back Cover
The latest edition of this bestselling biostatistics book is both comprehensive and easy to read. It provides a broad and practical overview of the statistical analysis methods used by researchers to collect, summarize, analyze, and draw conclusions from biological research data. The Fourth Edition can serve as either an introduction to the discipline for beginning students or a comprehensive procedural reference for today's practitioners.
CUSTOMER REVIEWS
More useful than other beginner's texts but..., July 16, 2006
As someone who recently retired from analyzing ecological data after a decade of it, I found this book to be pretty good onestop shopping. I wouldn't say it's an introduction to stats so much as it is a systematic compilation of all the "traditional" statistical topics (ttests, regressions, etc). As such, it contained some useful formulas that do not occur in regular "Stats 101" texts, such as sample size estimators for various analyses.
However, there are two things it is missing. As mentioned by other reviewers, there's no coverage (in the edition I have, anyway) of iterative techniques like bootstrapping, Monte Carlo approaches, etc. Those are coming up a lot in everyday statistical work these days.
More important is something missing from nearly EVERY beginning statistics text (and, often, from college education), which is the place of statistical testing in scientific logic. Too many beginners with statistics get stuck on fishing for significant differences in a stale old dataset rather than really thinking about their subject matter. In the absence of context, statistical "significance" can be deceptive and meaningless. One place to start on this subject is with Murphy & Myor's really good book called Statistical Power Analysis. I learned a ton from that book, which is a good companion for nearly any regular stats text. Happy crunching..
Rating: 4.0  Added on: 28 Jan 2007