A video has gone on Youtube called Professor Risk as part of a series made for the Cambridge 800 years anniversary. To be honest it's a bit lightweight when it comes to risk, and all the subtle bits got cut. We filmed a whole lot more including my GP taking my blood, discussing statins and screening for prostate cancer. All on the cutting-room floor. Good point: Stephen Fry does the intro voiceover. Bad point: me in my jim-jams.
This appeared in the Times on November 3rd and is based on the excellent lecture that got David Nutt sacked as chair of the Advisory Council for the Misuse of Drugs (although he might still be in post if he had only added that it was delivered in his personal capacity). Nutt has suffered the consequences of repeatedly breaking the taboo of comparing the risks of the legal and wholesome (horse-riding) or long-established (alcohol and smoking), with the illegal and "impure" Ecstasy and cannabis.
The version below contains links to sources and a few comments and corrections.
“Brits three romps from celeb sex” headlined the Sun on Friday, adding that “boffins at Cambridge University have calculated every person in the UK is linked to a star through their sexual partners — with just three steps separating the average person from a steamy celeb romp”.
Random events tend to cluster, and just to illustrate this we've suddenly got a burst of coverage of our work:
To coincide with the kick off of the football Premier League 2009–2010 we have updated our articles on the role of chance in football, and we have updated our animation to include leagues from across Europe over the past twenty years.
Is football just a matter of luck? Just because a team ends up top of the league, does it really mean it is the best team? We have taken most of the major league football games played in Europe since 1993 and created an animation that shows what happened in each league and how much of the apparent difference between the teams was due to chance alone.
This is a rather late announcement of pages we have put up on the use of screening tests. Using lie detectors, breast cancer and HIV screening as examples, we show how an apparently accurate test, when applied to a group of people in which only a small proportion have the thing you are trying to detect, will generate many false positives.