Sunday Data/Statistics Link Roundup (10/7/12)07 Oct 2012
- 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).