Sunday Data/Statistics Link Roundup (11/11/12)

  1. Statisticians have been deconstructed! I feel vaguely insulted, although I have to admit I’m not even sure I know what the article says. This line is a doozy though: "Statistics always pulls back from the claims it makes…" As a statistician blogger, I make tons of claims. I probably regret some of them, but I’d never take them back :-). 
  2. Following our recent detour into political analysis, Here is a story about the statisticians that helped Obama win the election by identifying blocks of voters/donors that could help lead the campaign to victory. I think there are some lessons here for individualized health. 
  3. XKCD is hating on frequentists! Wasserman and Gelman respond. This is the same mistake I think a lot of critics of P-values make. When used incorrectly, any statistical method makes silly claims. The key is knowing when to use them, regardless of which kind you prefer. 
  4. Another article in the popular press about the shortage of data scientists, in particular “big data” scientists. I also saw a ton of discussion of whether Nate Silver used “big data” in making his predictions. This is another one of those many, many cases where the size of the data is mostly irrelevant; it is knowing the right data to use that is important. 
  5. Apparently math can be physically painful.  I don’t buy it. 

Sunday data/statistics link roundup (4/29)

  1. Nature genetics has an editorial on the Mayo and Myriad cases. I agree with this bit: “In our opinion, it is not new judgments or legislation that are needed but more innovation. In the era of whole-genome sequencing of highly variable genomes, it is increasingly hard to justify exclusive ownership of particularly useful parts of the genome, and method claims must be more carefully described.” Via Andrew J.
  2. One of Tech Review’s 10 emerging technologies from a February 2003 article? Data mining. I think doing interesting things with data has probably always been a hot topic, it just gets press in cycles. Via Aleks J. 
  3. An infographic in the New York Times compares the profits and taxes of Apple over time, here is an explanation of how they do it. (Via Tim O.)
  4. Saw this tweet via Joe B. I’m not sure if the frequentists or the Bayesians are winning, but it seems to me that the battle no longer matters to my generation of statisticians - there are too many data sets to analyze, better to just use what works!
  5. Statistical and computational algorithms that write news stories. Simply Statistics remains 100% human written (for now). 
  6. The 5 most critical statistical concepts.