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Experimental Design

This guide provides an overview of essential principles for designing effective experiments in scientific research. It covers the importance of suitable controls, appropriate comparisons, and statistical analyses to enhance validity and reduce errors. Additionally, it addresses critical aspects such as sample size, data presentation, and ethical considerations regarding animal use. By understanding the formulation of hypotheses, controlling for variability and bias, and employing robust data analysis techniques, researchers can optimize their experimental designs for reliable outcomes.

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Experimental Design

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  1. Experimental Design Dr MP Seed m.p.seed@qmul.ac.uk

  2. Objectives • To be able to design an experiment taking into account: • Suitable controls • Suitable comparisons • Suitable statistics • Error reduction • Power • If animals are used, benefit/suffering • Final Data Presentation

  3. Science is a process of discovery through hypothesis testing

  4. Formulation of Hypothesis or objective Experimental Unit Control Variability Control of bias Treatment choice (independent variables) End Point choice (dependent variable) Choice of design Sample size Statistical analysis planning Pilot study Protocols and SOPs (Standard Operating Procedures) Statistical Analysis Interpretation Data Presentation Choice of ‘model’

  5. Common Errors: Lack of design – ‘Ad Hoc’ approach Historical controls Clue: variable numbers Factorial Design unbalanced Experiment size ‘Blocking’ Genetic heterogeneity (Inbred/outbred animals strains) Statistical analysis errors Assay errors

  6. Statistics – Parametric: The Bloody Obvious Test (t-Test) ANOVA Followed by Post Hoc test Bonferroni (No more than 4 comparisons unless corrected Neuman Keuls test (does not give confidence limits) Tuley Kramer (Gives confidence Limits) Comparison of groups to one control- Dunnett’s Test

  7. T-test Compares two groups Reduced reliability with multiple comparisons Bonferroni also, use correction factor or do not use for more than 4 comparisons.

  8. ANOVA Post Hoc Testing: Bonferroni, Newman Keul’s, Tukey Kramer Parametric

  9. Non Parametric ANOVA: • Kruskall Wallace (NOT Neuman Keuls as mentoned in lecture!!) • Followed by Post Hoc test eg Dunn’s test

  10. Power analysis Effect size 10-20% Standard deviation Significance level p<0.05 Power level 80-90% Sample size unknown? Alternative hypothesis 1 / 2 tailed?

  11. Useful Packages: Graphpad Prizm (Desktop) Minitab (Desktop) SPS Contain useful guides

  12. Reading Material

  13. Handout: Basic Principles of the Design of Animal Experiments. Festing et al, The Design of Animal Experiments, Laboratory Anumal Handbooks No14 Chapter 1.

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