Design of experiments and analysing experimental data

Date: 16 June 2020
Time: 9am-5pm
Instructor: Rosa Parisi and Christos Grigoroglou
Level: Introductory
Fee: £195 (£140 for those from educational, government and charitable institutions).

CMI offers up to five subsidised places at a reduced rate of £60 per course day to research staff and students within Humanities at The University of Manchester. These places are awarded in order of application. 

Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.


This course is designed for those interested in the design, conduct, and analysis of experiments. The course will examine how to design and analyse the experimental data in the context of medical statistics.

We will discuss various designs and their respective differences, advantages, and disadvantages. The course includes a review of statistics background that is needed for conducting and analysing experiments. We will start discussing how to summarise data, confidence intervals and hypothesis testing and illustrate analysis techniques such as t-test, Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA).

In addition, basic models with one independent variable and more complex models with two or more independent variables will be explained in greater detail. Stata and/or R software will be used to analyse the data.


After attending the course you should be able to:

  • Understand the principles of experimental design
  • Understand different types of experimental design
  • Know how to design experiments that are likely to yield valid results
  • Understand the logic of hypothesis testing
  • Have a basic understanding of the most common techniques used to analyse experimental data
  • Be able to select appropriate statistics to test the proposed hypothesis
  • Gain an understanding of how experiments can be used in business settings and help to increase profits and build competitive advantage. 


Course attendees should be familiar with statistical software.

Recommended reading 

  • Mitchell, Mark L. and Janina M. Jolley (2012) Research Design Explained. Elmont, CA; Wadsworth Publishing Co Inc.
  • Labrecque, Lauren I.  and George R. Milne (2012) Exciting red and competent blue: the importance of colour in marketing. Journal of the Academy of Marketing Science, 40(5), pp 711-727

About the instructor