Statistical design is one of the fundamentals of our subject, being at the core of the growth of statistics during the previous century. In this book the basic theoretical underpinnings are covered. It describes the principles that drive good designs and good statistics.
Design played a key role in agricultural statistics and set down principles of good practice, principles that still apply today. Statistical design is all about understanding where the variance comes from, and making sure that is where the replication is. Indeed, it is probably correct to say that these principles are even more important today.
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Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks.
George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in many aspects of statistics, having contributed to theoretical statistics in the areas of decision theory and statistical confidence, to environmental statistics, and has more recently concentrated efforts in statistical genomics. He also maintains active research interests in the theory and application of Monte Carlo and other computationally intensive methods. He is listed as an ISI "Highly
In other capacities, Professor Casella has served as Theory and Methods Editor of the Journal of the American Statistical Association, 1996-1999, Executive Editor of Statistical Science, 2001-2004, and Co-Editor of the Journal of the Royal Statistical Society, Series B, 2009-2012. He has served on the Board of Mathematical Sciences of the National Research Council, 1999-2003, and many committees of both the American Statistical Association and the Institute of Mathematical Statistics. Professor Casella has co-authored five textbooks: Variance Components, 1992; Theory of Point Estimation, Second Edition, 1998; Monte Carlo Statistical Methods, Second Edition, 2004; Statistical Inference, Second Edition, 2001, and Statistical Genomics of Complex Traits, 2007.Review:
From the reviews:
"In an era where many design texts present a wide collection of tools and practical considerations for creating designs, this book is a marked contrast with a primary focus on developing a thorough understanding of the core of design theory.... The book is an excellent reference for those already familiar with design of experiments, because of its careful and detailed presentation of core designs and how to verify that an appropriate analysis is chosen to match the structure of how the data were collected. In addition it contains numerous nuggets of wisdom about potential pitfalls from inattention to detail.... Overall, the style of the book gives a clear, understandable presentation of the formal details of statistical design for many core design types for balanced data involving categorical factors. The mathematical detail and rigor of the text allows students the opportunity to form a firm foundation on which to build their understanding and intuition about this important area. (Christine ANDERSON-COOK, JASA, June 2009, Vol. 104, No. 486)
"The goal is to describe the principles that drive good design, which are also the principles that drive good statistics’. ... Casella succeeds exceptionally well to reach his goals. I greatly enjoyed browsing through this book. The author’s experience and writing skills together make this an excellent course book. Concepts are presented in a very reader-friendly and instructive way. ... The layout of the book, huge amount of examples, and very clear writing make this a book highly recommended for anyone interested in statistical design." (Simo Puntanen, International Statistical Review, Vol. 76 (3), 2008)
"Overall, I found reading this book to be worthwhile. I particularly think the author’s discussion of blocking is quite interesting as well as the discussion of loop designs versus balanced incomplete block designs. In fact, I intend to use this book as supplementary reading material for my own design course." (Biometrics, December 2008)
“The level of this text is for the first or second year graduate students and, the material is about right for a one-semester course. The chapters cover, for the most part, the standard material of a book on designs, with mostly real examples, and applications of designs in real situations. ... This is an important book permitting to understand statistical designs.” (T. Postelnicu, Zentralblatt MATH, Vol. 1181, 2010)
“This book is a graduate-level text on the design of experiments. It is the product of the author’s statistical consulting experience as well as his experience teaching statistical design. It is intended for use in a one-semester course with first- or second-year graduate students who are familiar with standard statistical methods such as analysis of variance (ANOVA), blocking, and multiple regression. ... is worth considering for a graduate course in design for students with a good mathematical statistics background.” (William I. Notz, Mathematical Reviews, Issue 2011 k)
“This is one of the most simple and clearly written experimental design books ... . technical notes at the end of most of the chapters are great ways to let students understand the foundation of the materials conveyed in the book. ... Readers can quickly search information, discussions, comments, and answers to questions at the end of the chapters. ... Overall, the book seems thin, but it does include important information about statistical design. ... I enjoy reading this well-explained book.” (Keying Ye, Technometrics, Vol. 52 (4), November, 2010)
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