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. 

Sunday data/statistics link roundup (7/1)

  1. A really nice explanation of the elements of Obamacare. Rafa’s post on the new inHealth initiative Scott is leading got a lot of comments on Reddit. Some of them are funny (Rafa’s spelling got rocked) and if you get past the usual level of internet-commentary politeness, some of them seem to be really relevant - especially the comments about generalizability and the economics of health care. 
  2. From Andrew J. a cool visualization of the human genome, they are showing every base of the human genome over the course of a year. That turns out to be about 100 bases per second. I think this is a great way to show how much information is in just one human genome. It also puts the sequencing data deluge in perspective. We are now sequencing thousands of these genomes a year and its only going to get faster. 
  3. Cosma Shalizi has a nice list of unsolved problems in statistics on his blog (via Edo A.). These problems primarily fall into what I call Category 1 problems in my post on motivating statistical projects. I think he has some really nice insight though and some of these problems sound like a big deal if one was able to solve them.
  4. A really provocative talk on why consumers are the job creators. The issue of who are the job creators seems absolutely ripe for a thorough statistical analysis. There are a thousand confounders here and my guess is that most of the work so far has been Category 2 - let’s use convenient data to make a stab at this. But a thorough and legitimate data analysis would be hugely impactful. 
  5. Your eReader is collecting data about you.

Sunday data/statistics link roundup (5/27)

  1. Amanda Cox on the process they went through to come up with this graphic about the Facebook IPO. So cool to see how R is used in the development process. A favorite quote of mine, “But rather than bringing clarity, it just sort of looked chaotic, even to the seasoned chart freaks of 620 8th Avenue.” One of the more interesting things about posts like this is you get to see how statistics versus a deadline works. This is typically the role of the analyst, since they come in late and there is usually a deadline looming…
  2. An interview with Steve Blank about Silicon valley and how venture capitalists (VC’s) are focused on social technologies since they can make a profit quickly. A depressing/fascinating quote from this one is, “If I have a choice of investing in a blockbuster cancer drug that will pay me nothing for ten years,  at best, whereas social media will go big in two years, what do you think I’m going to pick? If you’re a VC firm, you’re tossing out your life science division.” He also goes on to say thank goodness for the NIH, NSF, and Google who are funding interesting “real science” problems. This probably deserves its own post later in the week, the difference between analyzing data because it will make money and analyzing data to solve a hard science problem. The latter usually takes way more patience and the data take much longer to collect. 
  3. An interesting post on how Obama’s analytics department ran an A/B test which improved the number of people who signed up for his mailing list. I don’t necessarily agree with their claim that they helped raise $60 million, there may be some confounding factors that mean that the individuals who sign up with the best combination of image/button don’t necessarily donate as much. But still, an interesting look into why Obama needs statisticians
  4. A cute statistics cartoon from @kristin_linn  via Chris V. Yes, we are now shamelessly reposting cute cartoons for retweets :-). 
  5. Rafa’s post inspired some interesting conversation both on our blog and on some statistics mailing lists. It seems to me that everyone is making an effort to understand the increasingly diverse field of statistics, but we still have a ways to go. I’m particularly interested in discussion on how we evaluate the contribution/effort behind making good and usable academic software. I think the strength of the Bioconductor community and the rise of Github among academics are a good start.  For example, it is really useful that Bioconductor now tracks the number of package downloads

Why does Obama need statisticians?

It’s worth following up a little on why the Obama campaign is recruiting statisticians (note to Karen: I am not looking for a new job!). Here’s the blurb for the position of “Statistical Modeling Analyst”:

The Obama for America Analytics Department analyzes the campaign’s data to guide election strategy and develop quantitative, actionable insights that drive our decision-making. Our team’s products help direct work on the ground, online and on the air. We are a multi-disciplinary team of statisticians, mathematicians, software developers, general analysts and organizers - all striving for a single goal: re-electing President Obama. We are looking for staff at all levels to join our department from now through Election Day 2012 at our Chicago, IL headquarters.

Statistical Modeling Analysts are charged with predicting electoral outcomes using statistical models. These models will be instrumental in helping the campaign determine how to most effectively use its resources.

I wonder if there’s a bonus for predicting the correct outcome, win or lose?

The Obama campaign didn’t invent the idea of heavy data analysis in campaigns, but they seem to be heavy adopters. There are 3 openings in the “Analytics” category as of today.

Now, can someone tell me why they don’t just call it simply “Statistics”?