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(More customer reviews)I was delighted with this book, because it fits some of my own prejudices about statistics!
We agree that the mechanics of statistical analysis are not the most important part of statistics for ecological studies. After all, for the last couple of decades the brunt of this has been borne by computers and software engineers. Much more important is that researchers understand what the computer output means. And Gotelli and Ellison devote most of their book to this.
Too many people collect data, then try to work out how to analyse it and what conclusions to draw. It's better to decide on the research question right at the start, then decide what kind of analysis is appropriate, and then what numbers you need to collect. The main part of this book is about this study design process.
In addition to the conventional frequentist approach, the book introduces Monte Carlo methods and Bayesian thinking. (I was interested to see that they reject non-parametric methods out of hand, recommending the use of Monte Carlo methods instead.) Moreover, they deal with parameter estimation and model building as well as hypothesis testing.
Written by ecologists for ecologists, it is remarkably clear and easy to read. You don't need much math to be able to follow the arguments, and numerical examples are there. (I for one can't cope with too much algebra; I need to see some numbers slotted in and results come out.) The final chapter is an exception, as it uses matrix algebra, but there's enough explanation of this in an appendix. Remember that the number crunching will be done by your statistical package: it will probably do things right if you ask it to do the right things, and this book is a guide to the right things to do with your data.
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A Primer of Ecological Statistics explains fundamental material in probability theory and experimental design for ecologists and environmental scientists. The book emphasizes a general introduction to probability theory and provides a detailed discussion of specific designs and analyses that are typically encountered in ecology and environmental science. Appropriate for use as either a stand-alone or supplementary text for upper-division undergraduate or graduate courses in ecological and environmental statistics, ecology, environmental science, environmental studies, or experimental design, the Primer also serves as a resource for environmental professionals who need to use and interpret statistics daily but have little or no formal training in the subject. The book is divided into three parts. Part I discusses the fundamentals of probability and statistical thinking. It introduces the logic and language of probability (Chapter 1), explains common statistical distributions used in ecology (Chapter 2) and important measures of central tendency and spread (Chapter 3), explains P-values, hypothesis testing, and statistical errors (Chapter 4), and introduces frequentist, Bayesian, and Monte Carlo methods of analysis (Chapter 5). Part II discusses how to successfully design and execute field experiments and sampling studies. Topics include design strategies (Chapter 6), a "bestiary" of experimental designs (Chapter 7), and transformations and data management (Chapter 8). Part III discusses specific analyses, and covers the material that is the main core of most statistics texts. Topics include regression (Chapter 9), analysis of variance (Chapter 10), categorical data analysis (Chapter 11), and multivariate analysis (Chapter 12). The book includes a comprehensive glossary, a mathematical appendix on matrix algebra, and extensively annotated tables and figures. Footnotes introduce advanced and ancillary material: some are purely historical, others cover mathematical/statistical proofs or details, and still others address current topics in the ecological literature.
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