Sunday Data/Statistics Link Roundup (7/22/12)22 Jul 2012
- This paper is the paper describing how Uri Simonsohn identified academic misconduct using statistical analyses. This approach has received a huge amount of press in the scientific literature. The basic approach is that he calculates the standard deviations of mean/standard deviation estimates across groups being compared. Then he simulates from a Normal distribution and shows that under the Normal model, it is unlikely that the means/standard deviations are so similar. I think the idea is clever, but I wonder if the Normal model is the best choice here…could the estimates be similar because it was the same experimenter, etc.? I suppose the proof is in the pudding though, several of the papers he identifies have been retracted.
- This is an amazing rant by a history professor at Swarthmore over the development of massive online courses, like the ones Roger, Brian and I are teaching. I think he makes some important points (especially about how we could do the same thing with open access in a heart beat if universities/academics through serious muscle behind it), but I have to say, I’m personally very psyched to be involved in teaching one of these big classes. I think that statistics is a field that a lot of people would like to learn something about and I’d like to make it easier for them to do that because I love statistics. I also see the strong advantage of in-person education. The folks who enroll at Hopkins and take our courses will obviously get way more one-on-one interaction, which is clearly valuable. I don’t see why it has to be one or the other…
- An interesting discussion with Facebook’s former head of big data. I think the first point is key. A lot of the “big data” hype has just had to do with the infrastructure needed to deal with all the data we are collecting. The bigger issue (and where statisticians will lead) is figuring out what to do with the data.
- This is a great post about data smuggling. The two key points that I think are raised are: (1) how when the data get big enough, they have their own mass and aren’t going to be moved, and (2) how physically mailing harddrives is still the fastest way of transferring big data sets. That is certainly true in genomics where it is called “sneaker net” when a collaborator walks a hard drive over to our office. Hopefully putting data in physical terms will drive home the point that the new scientists are folks that deal with/manipulate/analyze data.
- Not statistics related, but here is a high-bar to hold your work to: the bus-crash test. If you died in a bus-crash tomorrow, would your discipline notice? Yikes. Via C.T. Brown.