This richly illustrated book provides an overview of the design and
analysis of experiments with a focus on non-clinical experiments in the
life sciences, including animal research. It covers the most common
aspects of experimental design such as handling multiple treatment
factors and improving precision. In addition, it addresses experiments
with large numbers of treatment factors and response surface methods for
optimizing experimental conditions or biotechnological yields.
The book emphasizes the estimation of effect sizes and the principled
use of statistical arguments in the broader scientific context. It
gradually transitions from classical analysis of variance to modern
linear mixed models, and provides detailed information on power analysis
and sample size determination, including 'portable power' formulas for
making quick approximate calculations. In turn, detailed discussions of
several real-life examples illustrate the complexities and aberrations
that can arise in practice.
Chiefly intended for students, teachers and researchers in the fields of
experimental biology and biomedicine, the book is largely self-contained
and starts with the necessary background on basic statistical concepts.
The underlying ideas and necessary mathematics are gradually introduced
in increasingly complex variants of a single example. Hasse diagrams
serve as a powerful method for visualizing and comparing experimental
designs and deriving appropriate models for their analysis. Manual
calculations are provided for early examples, allowing the reader to
follow the analyses in detail. More complex calculations rely on the
statistical software R, but are easily transferable to other software.
Though there are few prerequisites for effectively using the book,
previous exposure to basic statistical ideas and the software R would be
advisable.
