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Addressing some of the fundamental issues that are behind designing good (pre-clinical) experiments, the bedrock of reproducible science.

Course Aim

This course aims to address some of the fundamental issues that are behind designing good experiments (focussing on pre-clinical experiments exclusively), the bedrock of reproducible science.

Arguably, all good experiments start with a good experimental design. Experimental design in the Biomedical Sciences is mostly about logic, common sense and the systematic application of relatively simple techniques to produce un-biased experimental results and reduce variation. This is good news because this should be relatively straightforward. Yet this is where the biggest blunders continue to be made, including by experienced biologists, with demonstrably expensive consequences on results.


The course will deal with those concepts, their links with statistical analyses generally and some of the traps that we have all fallen into. We will also address statistical issues relevant to animal research depending on the composition of the audience, in a session where the words “enjoyment” and “statistics” can hopefully share the same sentence.

Course Format

A series of interactive lectures and scenarios delivered by Manuel Berdoy, BMS, Oxford.

This course will be scheduled again in Hilary term on 6 March 2019. 

Course Content


At the end of the course, attendees should be able to:

  1. Describe some of the factors affecting reproducibility and external validity.
  2. List the different types of formal experimental designs (e.g. completely randomised, randomised block, repeated measures, Latin square and factorial experimental designs).
  3. Explain the concept of variability, its causes and methods of reducing it 
  4. Describe possible causes of bias and ways of alleviating it 
  5. Identify the experimental unit and recognise issues of non-independence (pseudo-replication). 
  6. Describe the six factors affecting significance, including the meaning of statistical power and “p-values”.
  7. Identify formal ways of determining sample size. 
  8. Explain the fundamental principles behind the output of an ANOVA, including “blocking” and “interactions”.

Course Length

One day

Participant Numbers

Maximum 50