Experimental Design for the Life Sciences

Höfundar: Graeme D. Ruxton; Nick Colegrave (Útgáfa: 4)
Experimental Design for the Life Sciences

Kaup valmöguleikar

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 Resources The online resources to accompany Experimental Design for the Life Sciences feature: 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.

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Útgefandi
Oxford University Press Academic UK
ISBN
9780191070730
Print ISBN
9780198717355
Format
Page Fidelity (PDF)
Útgáfa
4
Höfundar
Graeme D. Ruxton; Nick Colegrave
Tungumál
English
Útgefið
2016-12-01
Prent takmörkun á líftíma
100

Kaflar

  • Preface
  • Acknowledgements from the fourth edition
  • Contents
  • 1 Why you should 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
  • 1.5 Subjects, experimental units, samples, and terminology
  • Summary
  • 2 Starting with a well-defined hypothesis
  • 2.1 Why your study should be focused: questions, hypotheses, and predictions
  • 2.2 Producing the strongest evidence with which to challenge a hypothesis
  • 2.3 Controls
  • 2.4 The importance of a pilot study and preliminary data
  • Summary
  • 3 Selecting the broad design of your study
  • 3.1 Experimental manipulation versus natural variation
  • 3.2 Deciding whether to work in the field or the laboratory
  • 3.3 In vivo versus in vitro studies
  • 3.4 There is no perfect study
  • Summary
  • 4 Between-individual variation, replication, and sampling
  • 4.1 Between-individual variation
  • 4.2 Replication
  • 4.3 Selecting a sample
  • Summary
  • 5 Pseudoreplication
  • 5.1 Explaining what independence and pseudoreplication are
  • 5.2 Common sources of pseudoreplication
  • 5.3 Dealing with non-independence
  • 5.4 Accepting that sometimes replication is impractical
  • 5.5 Pseudoreplication, third variables, and confounding variables
  • 5.6 Cohort effects, confounding variables, and cross-sectional studies
  • Summary
  • 6 Sample size, power, and efficient design
  • 6.1 Selecting the appropriate number of replicates
  • 6.2 Factors affecting the power of an experiment
  • 6.3 Working out the power of a planned study
  • 6.4 Improving the power of a study
  • 6.5 Comparing the power of different planned studies
  • Summary
  • 7 The simplest type of experimental design: completely randomized, single-factor
  • 7.1 Completely randomized single-factor designs
  • 7.2 Randomization
  • 7.3 Factors with more than one level
  • 7.4 Advantages and disadvantages of complete randomization
  • Summary
  • 8 Experiments with several factors (factorial designs)
  • 8.1 Randomized designs with more than one factor
  • 8.2 Interactions
  • 8.3 Confusing levels and factors
  • 8.4 Split-plot designs (sometimes called split-unit designs)
  • 8.5 Latin square designs
  • 8.6 Thinking about the statistics
  • Summary
  • 9 Beyond complete randomization: blocking and covariates
  • 9.1 The concept of blocking on a particular variable
  • 9.2 Blocking on individual characters, space, and time
  • 9.3 The advantages and disadvantages of blocking
  • 9.4 Paired designs
  • 9.5 How to select blocks
  • 9.6 Covariates
  • 9.7 Interactions between covariates and factors
  • Summary
  • 10 Within-subject designs
  • 10.1 What do we mean by a within-subject design?
  • 10.2 The advantages of a within-subject design
  • 10.3 The disadvantages of a within-subject design
  • 10.4 Isn’t repeatedly measuring the same individual pseudoreplication?
  • 10.5 With multiple treatments, within-subject experiments can take a long time
  • 10.6 Which sequences should you use?
  • 10.7 Within-subject designs and randomized block designs
  • 10.8 It is possible to design experiments that mix within-subject and between-subject effects
  • Summary
  • 11 Taking measurements
  • 11.1 Calibration
  • 11.2 Inaccuracy and imprecision
  • 11.3 Sensitivity and specificity
  • 11.4 Intra-observer variability
  • 11.5 Inter-observer variability
  • 11.6 Deciding how to measure
  • 11.7 Pitfalls to avoid when recording data
  • Summary
  • Sample answers to self-test questions
  • Flow chart on experimental design
  • Bibliography
  • Index