I found this paper a couple years back when I was writing about statistical errors. I love the right turn on red example -- an everyday situation where bad statistics leads to extra deaths every year. (Somewhere on the order of 10-100, I think.)

The problem, turning "not statistically significant" into "there is no difference," happens all the time in just about every field of science. Often you see people report "three studies found that this medicine work, but two found that it didn't" and conclude that the evidence is contradictory and can't be trusted. But if you look at the effect sizes, you see the five studies found nearly the same answers -- its just two of them didn't quite cross the threshold for significance.

I wish I had a way of teaching statistical thinking more clearly than standard intro classes. It's so weird and counter-intuitive that very few people get it right. I've given it a shot by writing a book (http://www.statisticsdonewrong.com/) but there's a lot more to be done.