Robust Experimentation

A one day statistical meeting to discuss robust experimentation through design and analysis of experiments.

The goal of this event is to bring UK leaders in the field of design and analysis of experiments to provide an avenue for dissemination of the state-of-the-art in methodologies that underpin modern techniques in data collection and analysis, and hence to ensure that the results that statisticians and mathematicians provide to practitioners is robust. Statistical and mathematical modelling underpins much of the research that is done at Brunel (and of course worldwide), and mathematics and statistics tends to be seen as a tool that is used implicitly by researchers in many disciplines to get answers.

For example:

• Engineers will use a computer model to simulate catastrophic failure within an aircraft wing

• Health scientists will use population models to simulate the spread of COVID-19 under various lockdown scenarios

• Economists plot paths for financial systems under various fiscal interventions.

It turns out in many cases that small changes in model assumptions lead to very large changes in conclusions, and a major theme of the research within the newly-formed Brunel Centre for Mathematical and Statistical Modelling is assessing the quality of these models and how the scientific conclusions vary under different model assumptions, and data collection regimes.

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