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From the Publisher"… this primer on research design helps shed light on some of the fundamental issues in experimental design, analysis, and dissemination of results. Some of the most effective chapters are those regarding logistics and planning, how one should think about data and variation, how best to present results, and even how to respond to rejection. The author assumes some basic knowledge of mathematical statistics and the R programming language, and for readers with a solid research methods background, this primer will be a useful addition to their toolbox."
—Jo A. Wick, The American Statistician, November 2014
"This book is a general primer, with examples, explaining the value of statistical design and analysis of experiments. It uses the R computing language for all illustrative calculations and is written in a plain, down-to-earth, and easily understood manner."
—International Statistical Review, 2013
"Making the transition from student to professional researcher can be a daunting experience. This book can serve as a valuable refresher on hypothesis testing, coping with variation, data collection, sample size decisions and more, along with cursory explanation of R output largely based on freely available data sets. … This is high-level material to aid the reader in becoming a confident researcher … . For the reader who wants to put theory to practice, and do it in R, this work can be a guide to success in analyzing and collection categorical data, detecting confounding, bootstrap approaches, case-control and cohort studies, and more."
—Tom Schulte, MAA Reviews, April 2013
"… a nice primer for academic researchers. The book includes practical and straightforward information, if you like your statistics moderately seasoned with formulae and althorithms."
—Journal of Clinical Research Best Practices, June 2013