Communicating Research through Analogies
By Daniel Taylor, based on Professor Norman Fenton's appearance tonight on BBC4's “Climate Change by Numbers”.
A useful tool when communicating complex research to a non-specialist audience is to develop analogies explaining the concepts through the lens of a familiar or simplified situation. By shaping your work around a common interest, you invite the audience in and break down possible barriers by bridging what they don't know with something they do.
When scientists discovered the Higgs boson in 2012, deep below the Franco-Swiss border, the discovery was often described with an analogy developed by David Miller (UCL) using the metaphor of people entering a room, this was changed to incorporate physicists, students, Einstein and even Margaret Thatcher. One of the reasons this was so widely adopted is because of its ability to be shaped to suit the audience. If you're talking to footballers you could describe David Beckham walking into a room, to film enthusiasts, Morgan Freeman, to musicians, Paul McCartney, and so on.
It's something that Queen Mary does well, for example through Centre of the Cell and Digesting Science, which both explain science through vampires, dominos and other familiar concepts. Our latest public example of using metaphor to communicate research will be on BBC4 at 9:00 tonight, as Professor Norman Fenton describes the reasoning and meaning behind one of the key statistics behind climate change by using the analogy of the world's most popular sport, football.
Climate change is something that everyone is aware of, and something with the potential to affect all of our lives, but many do not know or understand the figures behind it, which act as a powerful force behind the argument to address its causes. By placing his explanation in the sphere of football, Professor Fenton creates a way to attract the interest of millions of football fans to communicate the importance of understanding the causes of the world's rising temperature. This ingenuity is a powerful way of describing the statistics behind the rhetoric by breaking down barriers, and simplifying concepts.
"The motivation for the programme was to take a new look at the climate change debate by focusing on three key numbers that all come from the most recent IPCC report. The numbers were:
- 0.85 degrees - the amount of warming the planet has undergone since 1880
- 95% - the degree of certainty climate scientists have that at least half the warming in the last 60 years is man-made
- one trillion tonnes - the cumulative amount of carbon that can be burnt, ever, if the planet is to stay below ‘dangerous levels’ of climate change
The idea was to get mathematicians/statisticians who had not been involved in the climate change debate to explain in lay terms how and why climate scientists had arrived at these three numbers. The other two presenters were Dr Hannah Fry (UCL) and Prof Sir David Spiegelhalter (Cambridge) and we were each assigned approximately 25 minutes on one of the numbers. My number was 95%.
In attempting to understand and explain how the climate scientists had arrived at their 95% figure I used a football analogy – both because of my life-time interest in football and because - along with my colleagues Anthony Constantinou and Martin Neil – we have worked extensively on models for football prediction. The climate scientists had performed what is called an “attribution study” to understand the extent to which different factors – such as human CO2 emissions – contributed to changing temperatures. The football analogy was to understand the extent to which different factors contributed to changing success of premiership football teams as measured by the total number of points they achieved season-by-season. In contrast to our normal Bayesian approach – but consistent with what the climate scientists did – we used data and classical statistical methods to generate a model of success in terms of the various factors. Unlike the climate models which involve thousands of variables we had to restrict ourselves to a very small number of variables (due to a combination of time limitations and lack of data). Specifically, for each team and each year we considered:
- Wages (this was the single financial figure we used)
- Total days of player injuries
- Manager experience
- Squad experience
- Number of new players
The statistical model generated from these factors produced, for most teams, a good fit of success over the years for which we had the data. Our ‘attribution study’ showed ‘Wages’ was by far the major influence. When Wages was removed from the study, the resulting statistical model was not a good fit. This was analogous to what the climate scientists’ models were showing when the human CO2 emissions factor was removed from their models; the previously good fit to temperature was no longer evident. And, analogous to the climate scientists’ 95% figure derived from their models, we were able to conclude there was a 95% chance that an increase in wages of 10% would result in at least one extra premiership point."
By Daniel Taylor
Assistant Public Engagement Officer
Queen Mary University of London