“There are three kinds of lies: Lies, damned lies, and statistics.” Few people know the struggle of correcting such lies better than David Spiegelhalter. Since 2007, he has been the Winton professor for the public understanding of risk (though he prefers “statistics” to ”risk”) at the University of Cambridge.

In a sunlit hotel room in Washington DC, Quartz caught up with Spiegelhalter recently to talk about his unique job. The conversation sprawled from the wisdom of eating bacon (would you swallow any other known carcinogen?), to the serious crime of manipulating charts, to the right way to talk about rare but scary diseases.

When he isn’t fixing people’s misunderstandings of numbers, he works to communicate numbers better so that misunderstandings can be avoided from the beginning. The interview is edited and condensed for clarity.

*Quartz:* You have one of the most unique jobs in the world. What does your job involve?

*Spiegelhalter:* Most of the time I’m working on quantitative and qualitative evidence. I give a lot of talks, write books, and advise people who want to communicate numbers. I also get called by the media to talk about numbers and whether we can believe them.** **So although my post is called “professor for the public understanding of risk,” I interpret it as professor for the public understanding of statistics.

In terms of research, my work is mostly collaborative, working with psychologists, mathematicians, and others who are trying to find ways to communicate risk. My current project, for example, is working on a website for families with babies that have congenital heart disease.

What we are communicating are simple statistical issues, such as underlying risk, standard errors, and variability. But they are extremely difficult to communicate clearly, even to people with some training in statistics. So we spend a lot of time with patient groups, changing wording after wording, such that we end up with something that is understandable without being technical or misleading.

**What’s a recent example of misrepresentation of statistics that drove you bonkers?**

I got very grumpy at an official graph of British teenage pregnancy rates** **that apparently showed they had declined to nearly zero. Until I realized that the bottom part of the axis had been cut off, which made it impossible to visualize the (very impressive) 50% reduction since 2000.