"Provides an excellent introduction to the methodology and implementation of experimentation in the natural, engineering and medical sciences. [...] This book focuses on general research skills, such as adopting a scientific mindset, learning how to plan meaningful experiments and understanding the fundamentals of collecting and interpreting data. It is directed to anyone engaged in experiments." Particularly suitable for undergraduates or other novice researchers learning about designing their own experiments.
"The tools to choose the graphs and statistics that are suitable for your data, and to understand what the statistical results actually mean." A very short introduction to statistics and why you need to use them. It also includes some guidance on gathering data, focused for the social sciences, but the principles are broadly applicable.
"Studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts." Recommended if you are considering designing an experiment but want a broad overview before choosing an approach, or if you have an approach but want to understand how it fits into the broader statistical and scientific thought process. The text is organized around case studies for different methods. It will likely be most applicable for undergraduates or graduate students learning about experimental design and who are comfortable with seeing equations scattered throughout the text.
The authors "motivate learning with interesting biological and medical examples; they emphasize intuitive understanding; and they focus on real data. The book covers basic topics in introductory statistics, including graphs, confidence intervals, hypothesis testing, comparison of means, regression, and designing experiments." Available on reserves.
"This handbook explains how to establish the framework for an experimental project, how to set up all of the components of an experimental system, design experiments within that system, determine and use the correct set of controls, and formulate models to test the veracity and resiliency of the data." A philosophical guide to experimental design, rather than a statistically focused approach.
"For students or researchers in biology who need to design experiments, sampling programs, or analyze resulting data. [...] The chapters include such topics as linear and logistic regression, simple and complex ANOVA models, log-linear models, and multivariate techniques. The main analyses are illustrated with many examples from published papers and an extensive reference list to both the statistical and biological literature is also included."