Simply Statistics A statistics blog by Rafa Irizarry, Roger Peng, and Jeff Leek

Sunday Data/Statistics Link Roundup (9/16/12)

  1. There has been a lot of talk about the Michael Lewis (of Moneyball fame) profile of Obama in Vanity fair. One interesting quote I think deserves a lot more discussion is: “On top of all of this, after you have made your decision, you need to feign total certainty about it. People being led do not want to think probabilistically.” This is a key issue that is only going to get worse going forward. All of public policy is probabilistic - we are even moving to clinical trials to evaluate public policy
  2. It’s sort of amazing to me that I hadn’t heard about this before, but a UC Davis professor was threatened for discussing the reasons PSA screening may be overused. This same issue keeps coming up over and over - screening healthy populations for rare diseases is often not effective (you need a ridiculously high specificity or a treatment with almost no side effects). What we need is John McGready to do a claymation public service video or something explaining the reasons screening might not be a good idea to the general public. 
  3. A bleg - I sometimes have a good week finding links myself and there are a few folks who regularly send links (Andrew J., Alex N., etc.) I’d love it if people would send me cool links when they see them with the email title, “Sunday LInks” - i’m sure there is more cool stuff out there. 
  4. The ICSB has a competition to improve the coverage of computational biology on Wikipedia. Someone should write a surrogate variable analysis or robust multiarray average article. 
  5. I had not hear of the ASA’s Stattrak until this week, it looks like there are some useful resources there for early career statisticians. With the onset of fall, it is closing in on a new recruiting season. If you are a postdoc/student on the job market and you haven’t read Rafa’s post on soft vs. hard money, now is the time to start brushing up! Stay tuned for more job market posts this fall from Simply Statistics.