What do all these things have common?
I've just finished reading Nate Silver's excellent book "The Signal and the Noise. Why so many predictions fail- but some don't". Nate is a Bayesian statistician who has had a very unusual career, amongst his claims to fame, he developed PECOTA, a system for predicting future performance of baseball players, he spent a couple of years making a lucrative living playing online poker, and he now runs a highly successful blog FiveThirtyEight which provides statistical analysis of political polling data. I first started following FiveThirtyEight during the 2008 US presidential campaign and have been a regular reader ever since then. Nate's now become a celebrity with his uncannily accurate predictions of both the 2008 and 2012 US presidential elections.
Since I work in scientific fields that rely on empirical analysis of massively large data sets, and we are also moving towards undertaking iterative modeling of both bacterial communities and the metabolism of individual bacterial cells, I found Nate's book fascinating. And I think the take home message is very important for us- that anyone doing modeling or forecasting should avoid overconfidence, and recognise the degree of uncertainty in one's models or predictions.