Jack Welch got a little conspiracy-theory crazy with the job numbers. Thomas Lumley over at StatsChat makes a pretty good case for debunking the theory. I think the real take home message of Thomas’ post and one worth celebrating/highlighting is that agencies that produce the jobs report do so based on a fixed and well-defined study design. Careful efforts by government statistics agencies make it hard to fudge/change the numbers. This is an underrated and hugely important component of a well-run democracy.
On a similar note Dan Gardner at the Ottawa Citizen points out that evidence-based policy making is actually not enough. He points out the critical problem with evidence: in the era of data what is a fact? “Facts” can come from flawed or biased studies just as easily from strong studies. He suggests that a true “evidence based” administration would invest more money in research/statistical agencies. I think this is a great idea.
An interesting article by Ben Bernanke suggesting that an optimal approach (in baseball and in policy) is one based on statistical analysis, coupled with careful thinking about long-term versus short-term strategy. I think one of his arguments about allowing players to play even when they are struggling short term is actually a case for letting the weak law of large numbers play out. If you have a player with skill/talent, they will eventually converge to their “true” numbers. It’s also good for their confidence….(via David Santiago).
Here is another interesting peer review dust-up. It explains why some journals “reject” papers when they really mean major/minor revision to be able to push down their review times. I think this highlights yet another problem with pre-publication peer review. The evidence is mounting, but I hear we may get a defense of the current system from one of the editors of this blog, so stay tuned…
Several people (Sherri R., Alex N., many folks on Twitter) have pointed me to this article about gender bias in science. I initially was a bit skeptical of such a strong effect across a broad range of demographic variables. After reading the supplemental material carefully, it is clear I was wrong. It is a very well designed/executed study and suggests that there is still a strong gender bias in science, across ages and disciplines. Interestingly both men and women were biased against the female candidates. This is clearly a non-trivial problem to solve and needs a lot more work, maybe one step is to make recruitment packages more flexible (see the comment by Allison T. especially).