Foundational and Applied Statistics for Biologists Using R

This is a terrific intermediate level modern applied statistics text for biologists… or anyone else who is interested in data analysis. It is a big, handsome book of 600 plus pages. That might seem annoyingly large, but it sure does make it easy to do a thorough job of introducing and detailing the main concepts, the methods, and the pleasures of modern data analysis. It is a visually pleasing book with good layouts, nice typefaces and great tables and graphics… and the R code to produce them! A great way for a class to really engage with R graphics.

The Table of Contents shows 7 foundational chapters followed by 4 applications chapters. The first chapter on philosophical and historical foundations of science and “knowing” is novel, well done and too brief! But I deeply appreciated its presence, its importance for students and the opportunity it provides for making connections between fields. The remaining 6 foundational chapters present the traditional set of concepts: probability, density functions, their parameters and statistics, interval estimation (by sampling, resampling and simulation), hypothesis testing, sampling design and experimental design. The following application chapters are the classic core of statistical models: regression, ANOVA, and contingency counts.

The author has put effort into making the book. A website and a companion R package, asbio, serve two audiences: introductory classes and more advanced classes. He has succeeded nicely in writing a dual level book. Selective skips and deletes will still leave a well written introductory text with engaging worked examples each offering the opportunity to develop one’s R skills. For my more mature students there is ample advanced material, many references to the literature, and still those many worked examples with advanced questions to both exercise their statistical understanding and also develop their R skills in a modern analytic environment.

As an example, the book discusses the advanced concept of sphericity in both introductory and advanced contexts. It then shows how easy it is to find and grab a repeated measures ANOVA procedure from an R package off the internet which addresses the issues of sphericity and how to make the adjustments calculations needed. This is one of those many worked examples I mentioned earlier. It uses readily available R code. It proved to be just what I needed to help convince a colleague to leave her old, expensive statistical computing environment for the freedom of R. How nice to have an introductory book that includes grown-up conversations about advanced ideas!

The book is well produced and I enjoyed the companion website, http://www2.cose.isu.edu/~ahoken/book/. I would strongly recommend the book for mature students, such as those who have engaged and declared a major in bio/enviro/ecosystem studies programs. It is not a book I’d pick for an introductory survey course. I look forward to using it with my upper level undergrads and the Masters and PhD students I continue to work with.

Mike Sutherland is a semi retired statistical consultant who works on interesting academic and business problems. He was a founding faculty member of Hampshire College, then moved to the University of Massachusetts to become the Director of the Statistical Consulting Center.