Experimental Design for the Life Sciences

The only undergraduate textbook devoted to experimental design for the life sciences, making this essential aspect of the research process readily understandable.

The careful design of experiments lies at the core of good research. Experimental Design for the Life Sciences equips you with the skills you need to effectively design experiments, making this essential aspect of the research process readily understandable. It demonstrates how good experimental design relies on clear thinking and biological understanding, not mathematical or statistical complexity.

With a refreshingly approachable and articulate style, the book walks you through the considerations that go into designing an experiment in clear, practical terms. Using examples drawn from across the life sciences - from ecology, biochemistry, molecular biology, genetics, and health sciences - the authors illustrate how these concepts are applied within the broad context of real biological research.

Online Resource Centre
The Online Resource centre to accompany Experimental Design for the Life Sciences features:

For students:
· Self-test questions and answers
· Additional examples
· Supplementary sections discuss complex concepts and statistical issues in more depth
· Links to useful websites and free software

For lecturers:
· Suggested course structures, complete with practical exercises· Figures from the book, available to download

A well-presented, clearly argued, concise and witty introduction to one of the most important topics in the life sciences.

Auteur

Graeme D. Ruxton, Professor, University of St Andrews, Nick Colegrave, Lecturer in Biology, The University of Edinburgh

Contenu

• 1: Why you should care about design

• 2: Starting with a well-defined hypothesis

• 3: Selecting the broad design of your study

• 4: Between-individual variation, replication, and sampling

• 5: Pseudoreplication

• 6: Sample size, power, and efficient design

• 7: The simplest type of experimental design: completely randomized single-factor

• 8: Experiments with several factors (factorial designs)

• 9: Beyond complete randomization: blocking and covariates

• 10: Within-subject designs

• 11: Taking measurements