Efnisyfirlit

• 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

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