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title author date number-sections
Experimental Design
Vicki Hodgson, Martin van Rongen
today
false

Overview

This one-day course is designed to complement training in statistical analysis, and focuses on how to design effective experiments while bearing in mind the planned analysis.

Included topics:

  • Setting a good research question
  • Choosing & defining variables
  • Confounding variables
  • Independence & pseudoreplication
  • Revisiting statistical power
  • Case study examples, for discussion

::: callout-tip

Learning Objectives

  • Feel confident designing experiments with statistical analysis in mind
  • Understand common "pitfalls" that occur when designing experiments, and how to avoid or combat them
  • Apply these skills to at least one case study example :::

Prerequisites

Knowledge of core statistical concepts, including the statistical inference framework, linear modelling and power analysis, are required for the course. We recommend that students have attended the Core Statistics course or an equivalent.

Some of the course materials have been created using R; users may wish to follow along by copying the code themselves. If so, knowledge of statistical analysis in R is preferred.

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### Exercises

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 | {{< fa solid star >}} {{< fa regular star >}} {{< fa regular star >}} | Exercises in level 1 are simpler and designed to get you familiar with the concepts and syntax covered in the course. |
 | {{< fa solid star >}} {{< fa solid star >}} {{< fa regular star >}} | Exercises in level 2 combine different concepts together and apply it to a given task. |
 | {{< fa solid star >}} {{< fa solid star >}} {{< fa solid star >}} | Exercises in level 3 require going beyond the concepts and syntax introduced to solve new problems. |
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Authors

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About the authors:

  • Vicki Hodgson
    Affiliation: Bioinformatics Training Facility, University of Cambridge
    Roles: writing - original draft; conceptualisation; coding; creation of synthetic datasets
  • Martin van Rongen
    Affiliation: Bioinformatics Training Facility, University of Cambridge
    Roles: writing; conceptualisation; coding
<!--## Acknowledgements

With thanks to CRUK Experimental Design -->