ISBN-10:
019928511X
ISBN-13:
9780199285112
Pub. Date:
06/28/2006
Publisher:
Oxford University Press, USA
Experimental Design for the Life Sciences / Edition 2

Experimental Design for the Life Sciences / Edition 2

by Graeme D. Ruxton

Paperback

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Overview

"This book aims to teach the reader how to design effective experiments. The overwhelming majority of life scientists design experiments. However they tend to approach design in an informal ad hoc way, improving their techniques in the light of experience. This book aims to provide the junior scientist a short-cut way to learn how to design effective experiments without going through a painful trial and error process. For more experienced scientists, the text should also function to stimulate them to think about the way they design experiments, and perhaps lead them to design more effective experiments in future. Concepts, such as power analysis and pseudoreplication, that many experienced scientists consider to be mystifying or difficult, are explained in clear and practical terms. The emphasis throughout is to demonstrate that good experimental design is about clear thinking and biological understanding, not mathematical or statistical complexity. Companion Web Site All the figures from the book will be available to download free from the companion web site at www.oup.com/uk/best.textbooks/biology/ruxtoncolegrave"

Product Details

ISBN-13: 9780199285112
Publisher: Oxford University Press, USA
Publication date: 06/28/2006
Edition description: REV
Pages: 184
Product dimensions: 9.60(w) x 7.20(h) x 0.30(d)

About the Author

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

Table of Contents

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
9. Beyond complete randomization: blocking and covariates
10. Within-subject designs
11. Taking measurements

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