If it's worth saying, but not worth its own post (even in Discussion), then it goes here.
This seems like a big deal:
http://www.pnas.org/content/early/2013/10/28/1313476110.full.pdf
Basically, dude illustrates equivalence between p-values and Bayes factors and concludes that 17-25% of studies with a p-value acceptance threshold of 0.05 will be wrong. This implies that the lack of reproducibility in science isn't necessarily due to egregious misconduct, etc., but rather insufficiently strict statistical standards.
So is this new/interesting, or do I just naively think so because it's not my field?
Not a big deal. The estimate you're impressed by can be done from power and prior odds like in Ioannides's famous paper and are similar to Leek's estimates from p-value distributions, and the recommendations baffle me - increase alpha?! P-value hacking is part of how we got here in the first place!