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Experimental Design for the Life Sciences

Experimental Design for the Life Sciences - xxx edition

Experimental Design for the Life Sciences - xxx edition

Experimental Design for the Life Sciences by Graeme D. Ruxton and Nick Colegrave - ISBN
Edition: XXX
Copyright: 2003
Publisher: Oxford University Press
International: No
Experimental Design for the Life Sciences by Graeme D. Ruxton and Nick Colegrave - ISBN
Edition: XXX
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"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"

Table of Contents

Table of Contents

1 Why you need to care about design

1.1 Why experiments need to be designed
1.2 The costs of poor design
1.3 The relationship between experimental design and statistics
1.4 Why good experimental design is particularly important to life scientists

2 Starting with a well-defined hypothesis

2.1 Why your experiment should be focused: questions, hypotheses and predictions
2.2 Producing the strongest evidence with which to challenge a hypothesis
2.3 Satisfying sceptics
2.4 The importance of a pilot study and preliminary data
2.5 Experimental manipulation versus natural variation
2.6 Deciding whether to work in the field or the laboratory
2.7 There is no perfect study

3 Between-individual variation, replication and sampling

3.1 Between-individual variation
3.2 Replication
3.3 Pseudoreplication
3.4 Randomisation
3.5 Selecting an appropriate number of replicates

4 Different experimental designs

4.1 Controls
4.2 Completely randomised and factorial experiments
4.3 Blocking
4.4 Cross-over designs
4.5 Split-plot designs

5 Taking measurements

5.1 Calibration
5.2 Inaccuracy and Imprecision
5.3 Intra-observer variability
5.4 Inter-observer variability
5.5 Defining categories
5.6 Observer effects
5.7 Recording data
5.8 Computers and automated data collection

6 Final thoughts

6.1 How to select the levels for a treatment
6.2 Subsampling: more woods or more trees?
6.3 Using unbalanced groups for ethical reasons
6.4 Other sampling schemes
6.5 Latin square designs
6.6 More on interactions