Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of experiments and data analysis in the biological and agricultural sciences. The book presents statistical ideas in the context of biological and agricultural sciences to which they are being applied, drawing on relevant examples from the authors’ experience.
Taking a practical and intuitive approach, the book only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat® statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R.
By the time you reach the end of the book (and online material) you will have gained:
The book concludes with a guide to practical design and data analysis. It gives you the understanding to better interact with consultant statisticians and to identify statistical approaches to add value to your scientific research.
"…This is absolutely a traditional statistics textbook, with a strong basis in classical approaches such as blocking, crossed, and nested designs. … There is much the volume does well. It provides one of the most lucid descriptions of ANOVA that I have come across, and the strong emphasis on coupling experimental design with appropriately planned analysis (often described as important but rarely covered in detail in other statistical publications) make this a textbook that would be useful to those setting up controlled experiments, particularly in the field. Additionally, the provision of plenty of online material and exercises (at http://www.stats4biol.info) for use with GenStat, SAS, and R packages is a useful practical resource. … [T]o those who might be looking for a new statistics textbook to work through systematically, it does provide a good reference for anyone who wishes to dissect specific topics in more depth."
—Matthew R.E. Symonds in The Quarterly Review of Biology, June 2017
"This book is the first serious and successful attempt to teach the general principles underlying sound experimental design and analysis to an audience of students and researchers in biology. The book is written from a strongly applied perspective with lots of real-life examples, but enough mathematical details are given to allow the reader to tailor design and analysis principles to new problems.
The leading principle for analysis of experimental data is the multi-stratum analysis of variance. This powerful principle is part of the Rothamsted tradition of applied statistics to which the authors belong. This book makes statistical highlights from that tradition accessible to life scientists without demanding excessive mathematical skills."
—Fred van Eeuwijk, Wageningen University and Research Centre
"This book is easy to read. I am very happy to recommend this book to both scientists as a reference, and to students in the area of agricultural and biological plant sciences as a textbook. It covers most aspects of planning experiments and the steps necessary for analyzing the resulting data, bringing the authors’ experience to the reader with real examples. It is obvious that the authors have taken great care in framing their conclusions and the possible interpretations of the results."
—Clarice G.B. Demétrio, Professor of Experimental Statistics, Escola Superior de Agricultura "Luiz de Queiroz," University of São Paulo
"This book connects the underlying principles of design and statistics to good practice in data analysis. It also gives a solid account of the most commonly used and needed statistical methods in experimental biology. It explains concepts in clear, practical, and accessible ways, using real data to illustrate throughout. Mathematical notation is used when necessary but the explanations of key points are in common language.
I would particularly recommend this book for research students. It provides a no-nonsense, gimmick-free account of the key statistical concepts and practices that will allow you to do valid and useful analyses and understand the results from statistical software."
—Richard Coe, Principal Scientist-Research Methods, World Agroforestry Centre (ICRAF) and Statistical Services Centre
"This book will be invaluable to plant scientists who want to develop their knowledge of statistics. Based on courses developed by the authors, the aim is to provide a deep understanding of the most commonly used experimental designs and their analysis, along with linear regression, and to emphasize the connections between these areas. A background in basic statistics, to the level of t-tests, would be helpful, though a revision chapter is provided. Additional chapters introduce linear mixed models and generalised linear models for counts and proportions.
The authors’ extensive practical experience is apparent throughout, particularly in the many interesting examples that pervade the book. A companion website provides associated data and code in GenStat, R, and (soon) SAS. This in itself is a terrific resource, with 50 well-commented programs covering basic analyses and extensions. Each chapter has exercises; these are often challenging and solutions will be provided on the website.
Most of the examples stem from Rothamsted, where all of the authors work or have worked, and the book has something of a Rothamsted ‘feel’ to it, for example with a strong emphasis on multi-stratum ANOVA to incorporate blocking and other experimental structure even when this is not strictly essential; the benefits of this approach for clarifying more complex designs become apparent as the book progresses.
Everything is here for the plant scientist to develop a really solid understanding of the subject and it is difficult to see how the authors could have done a better job of delivering the book that they set out to write."
—Martin Ridout, Professor of Applied Statistics, University of Kent
Introduction. A Review of Basic Statistics
Principles for Designing Experiments
Models for a Single Factor
Checking Model Assumptions
Transformations of the Response
Models with Simple Blocking Structure
Extracting Information about Treatments
Models with Complex Blocking Structure
Replication and Power
Dealing with Non-Orthogonality
Models for a Single Variate: Simple Linear Regression
Checking Model Fit
Models for Several Variates: Multiple Linear Regression
Models for Variates and Factors
Incorporating Structure: Mixed Models
Models for Curved Relationships
Models for Non-Normal Responses: Generalized Linear Models
Practical Design and Data Analysis for Real Studies