This course is intended to fill the gap between the ‘how to’ knowledge in basic statistics courses and having a real understanding of what those statistics mean. It will also help attendees to make sense of the various alternative approaches presented in recent articles in HCI and wider scientific literature.

At the end of the course attendees will have a richer understanding of: the nature of random phenomena and different kinds of uncertainty; the different options for analysing data and their strengths and weaknesses; ways to design studies and experiments to increase ‘power’ – the likelihood of successfully uncovering real effects; and the pitfalls to avoid and issues to consider when dealing with empirical data.

Attendees will leave better able to design studies that efficiently use resources available and appropriately, effectively and reliably analyse the results

Intended Audience(s)

The course is intended for both experienced researchers and students who have already, or intend to engage in quantitative analysis of empirical data or other forms of statistical analysis. It will also be of value to practitioners using quantitative evaluation.


The course will assume some familiarity with statistical concepts theoretical or practical, for example, the use of t-tests or similar techniques. There will be occasional formulae, but the focus of the course is on conceptual understanding not mathematical skills.

Practical work

There will be occasional practical exercises, for example, coin tossing experiments … but no complex numerical calculations!