Mark Twain (Samuel Clemens) is quoted as having said "Figures don't lie, but liars figure". Here are a few links to help understand statistics and the numbers behind the news.
Tim Harford investigates numbers in the news. Numbers are used in every area of public debate. But are they always reliable? Tim and the "More or Less" team try to make sense of the statistics which surround us.
"Statistics Done Wrong" is a guide to the most popular statistical errors and slip-ups committed by scientists every day, in the lab and in peer-reviewed journals. Many of the errors are prevalent in vast swathes of the published literature, casting doubt on the findings of thousands of papers. "Statistics Done Wrong" assumes no prior knowledge of statistics, so you can read it before your first statistics course or after thirty years of scientific practice.
Nate Silver's web site uses data-driven analysis to understand the numbers behind the news in politics, economics, science, life and sports.
Formerly a feature of The New York Times it is now under the auspices of ESPN. Nate Silver gained fame for correctly predicting the winner of all 50 states and the District of Columbia in the 2012 presidential election.
The failure of forecasting is also due to the limits of learning from history. The models forecasters use are all built, to one degree or another, on the notion that historical patterns recur, and that the past can be a guide to the future. The problem is that some of the most economically consequential events are precisely those that haven't happened before. Think of the oil crisis of the 1970s, or the fall of the Soviet Union, or, most important, China's decision to embrace (in its way) capitalism and open itself to the West. Or think of the housing bubble. Many of the forecasting models that the banks relied on assumed that housing prices could never fall, on a national basis, as steeply as they did, because they had never fallen so steeply before. But of course they had also never risen so steeply before, which made the models effectively useless.