26 Jul 2013
Check out this really good piece over at the Independent. It talks about the rise of statisticians as rockstars, naming Hans Rosling, Nate Silver, and Chris Volinsky among others. I think that those guys are great and deserve all the attention they get.
I only hope that more of the superstars that fly under the radar of the general public but have made huge contributions to science/medicine (like Ross Prentice, Terry Speed, Scott Zeger, or others that were highlighted in the comments here) get the same kind of attention (although I suspect they might not want it).
I think one of the best parts of the article (which you should read in it’s entirety) is Marie Davidian’s quote:
There are rock stars, and then there are rock bands: statisticians frequently work in teams
22 Jul 2013
A few folks here at Hopkins were just reading the comments of our post on awesome young/senior statisticians. It was cool to see the diversity of opinions and all the impressive people working in our field. We realized that another question we didn’t have a great answer to was:
What are the 5 most influential statistics papers of the aughts (2000-2010)?
Now that the auggies or aughts or whatever are a few years behind us, we have the benefit of a little hindsight and can get a reasonable measure of retrospective impact.
Since this is a pretty broad question I’d thought I’d lay down some generic ground rules for nominations:
- Papers must have been published in 2000-2010.
- Papers must primarily report a statistical method or analysis (the impact shouldn’t be only because of the scientific result).
- Papers may be published in either statistical or applied journals.
For extra credit, along with your list give your definition of impact. Mine would be something like:
- Has been cited at a high rate in scientific papers (in other words, it is used by science, not just cited by statisticians trying to beat it)
- Has corresponding software that has been used
- Made simpler/changed the way we did a specific type of analysis
I don’t have my list yet (I know, a cop-out) but I’m working on it.
21 Jul 2013
- Let’s shake up the social sciences is a piece in the New York Times by Nicholas Christakis who rose to fame by claiming that obesity is contagious. Gelman responds that he thinks maybe Christakis got a little ahead of himself. I’m going to stay out of this one as it is all pretty far outside my realm - but I will say that I think quantitative social sciences is a hot area and all hot areas bring both interesting new results and hype. You just have to figure out which is which (via Rafa).
- This is both creepy and proves my point about the ubiquity of data. Basically police departments are storing tons of information about where we drive because, well, it is easy to do so why not?
- I mean, I’m not an actuary and I don’t run cities, but this strikes me as a little insane. How do you not just keep track of all the pensions you owe people and add them up to know your total obligation? Why predict it when you could actually just collect the data? Maybe an economist can explain this one to me. (via Andrew J.)
- [ 1. Let’s shake up the social sciences is a piece in the New York Times by Nicholas Christakis who rose to fame by claiming that obesity is contagious. Gelman responds that he thinks maybe Christakis got a little ahead of himself. I’m going to stay out of this one as it is all pretty far outside my realm - but I will say that I think quantitative social sciences is a hot area and all hot areas bring both interesting new results and hype. You just have to figure out which is which (via Rafa).
- This is both creepy and proves my point about the ubiquity of data. Basically police departments are storing tons of information about where we drive because, well, it is easy to do so why not?
- I mean, I’m not an actuary and I don’t run cities, but this strikes me as a little insane. How do you not just keep track of all the pensions you owe people and add them up to know your total obligation? Why predict it when you could actually just collect the data? Maybe an economist can explain this one to me. (via Andrew J.)
4.](http://www.nytimes.com/2013/07/19/opinion/in-defense-of-clinical-drug-trials.html?src=recg&gwh=9D33ABD1323113EF3AC9C48210900171) reverse scoops our clinical trials post! In all seriousness, there are a lot of nice responses there to the original article.
- JH Hospital back to #1. Order is restored. Read our analysis of Hopkins ignominious drop to #2 last year (via Sherri R.).
19 Jul 2013
At first blush the news out of San Jose State that the partnership with Udacity is being temporarily suspended is bad news for MOOCs. It is particularly bad news since the main reason for the suspension is poor student performance on exams. I think in the PR game there is certainly some reason to be disappointed in the failure of this first big experiment, but as someone who loves the idea of high-throughput education, I think that this is primarily a good learning experience.
The money quote in my mind is:
Officials say the data suggests many of the students had little college experience or held jobs while attending classes. Both populations traditionally struggle with college courses.
“We had this element that we picked, student populations who were not likely to succeed,” Thrun said.
I think it was a really nice idea to try to expand educational opportunities to students who traditionally dont have time for college or have struggled with college. But this represents a pretty major confounder in the analysis comparing educational outcomes between students in the online and in person classes. There is a lot of room for the ecological fallacy to make it look like online classes are failing. They could very easily address this problem by using a subset of students randomized in the right way. There are even really good papers - like this one by Glynn - on the optimal way to do this.
I think there are some potential lessons learned here from this PR problem:
- We need good study design in high-throughput education. I don’t know how rigorous the study design was in the case of the San Jose State experiment, but if the comparison is just whoever signed up in class versus whoever signed up online we have a long way to go in evaluating these classes.
- We need coherent programs online It looks like they offered a scattered collection of mostly lower level courses online (elementary statistics, college algebra, entry level math, introduction to programming and introduction to psychology). These courses are obvious ones for picking off with MOOCs since they are usually large lecture-style courses in person as well. But they are also hard classes to “get motivated for” if there isn’t a clear end goal in mind. If you are learning college algebra online but don’t have a clear path to using that education it might make more sense to start with the Khan Academy
- We need to parse variation in educational attainment. It makes sense to evaluate in class and online students with similar instruments. But I wonder if there is a way to estimate the components of variation: motivation, prior skill, time dedicated to the course, learning from course materials, learning from course discussion, and learning for different types of knowledge (e.g. vocational versus theoretical) using statistical models. I think that kind of modeling would offer a much more clear picture of whether these programs are “working”.
19 Jul 2013
The New York Times has published some letters to the Editor in response to the piece by Clifton Leaf on clinical trials. You can also see our response here.
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