Tag: academics

20
Sep

Every professor is a startup

There has been a lot of discussion lately about whether to be in academia or industry. Some of it I think is a bit unfair to academia. Then I saw this post on Quora asking what Hilary Mason’s contributions were to machine learning, like she hadn’t done anything. It struck me as a bit of academia hating on industry*. I don’t see why one has to be better/worse than the other, as Roger points out, there is no perfect job and it just depends on what you want to do. 

One thing that I think gets lost in all of this are the similarities between being an academic researcher and running a small startup. To be a successful professor at a research institution, you have to create a product (papers/software), network (sit on editorial boards/review panels), raise funds (by writing grants), advertise (by giving talks/presentations), identify and recruit talent (students and postdocs), manage people and personalities (students,postdocs, collaborators) and scale (you start as just yourself, and eventually grow to a group with lots of people). 

The goals are somewhat different. In a startup company, your goal is ultimately to become a profitable business. In academia, the goal is to create an enterprise that produces scientific knowledge. But in either enterprise it takes a huge amount of entrepreneurial spirit, passion, and hustle. It just depends on how you are spending your hustle. 

*Sidenote: One reason I think she is so famous is that she helps people, even people that can’t necessarily do anything for her. One time I wrote her out of the blue to see if we could get some Bitly data to analyze for a class. She cheerfully helped us get it, even though the immediate payout for her was not obvious. But I tell you what, when people ask me about her, I’ll tell them she is awesome. 

06
Feb

Sunday Data/Statistics Link Roundup (2/5)

  1. Cool app, you can write out an equation on the screen and it translates the equation to latex. Via Andrew G.
  2. Yet another D3 tutorial. Stay tuned for some cool stuff on this front here at Simply Stats in the near future. Via Vishal.
  3. Our favorite Greek statistician in the news again
  4. How measurement of academic output harms science. Related: is submitting scientific papers too time consuming? Stay tuned for more on this topic this week. Via Michael E. 
  5. One from the archives: Data visualization and art
17
Nov

Google Scholar Pages

If you want to get to know more about what we’re working on, you can check out our Google Scholar pages:

I’ve only been using it for a day but I’m pretty impressed by how much it picked up. My only problem so far is having to merge different versions of the same paper.

19
Oct

Do we really need applied statistics journals?

All statisticians in academia are constantly confronted with the question of where to publish their papers. Sometimes it’s obvious: A theoretical paper might go to the Annals of Statistics or JASA Theory & Methods or Biometrika. A more “methods-y” paper might go to JASA or JRSS-B or Biometrics or maybe even Biostatistics (where all three of us are or have been associate editors).

But where should the applied papers go? I think this is an increasingly large category of papers being produced by statisticians. These are papers that do not necessarily develop a brand new method or uncover any new theory, but apply statistical methods to an interesting dataset in a not-so-obvious way. Some papers might combine a set of existing methods that have never been combined before in order to solve an important scientific problem.

Well, there are some official applied statistics journals: JASA Applications & Case Studies or JRSS-C or Annals of Applied Statistics. At least they have the word “application” or “applied” in their title. But the question we should be asking is if a paper is published in one of those journals, will it reach the right audience?

What is the audience for an applied stat paper? Perhaps it depends on the subject matter. If the application is biology, then maybe biologists. If it’s an air pollution and health application, maybe environmental epidemiologists. My point is that the key audience is probably not a bunch of other statisticians.

The fundamental conundrum of applied stat papers comes down to this question: If your application of statistical methods is truly addressing an important scientific question, then shouldn’t the scientists in the relevant field want to hear about it? If the answer is yes, then we have two options: Force other scientists to read our applied stat journals, or publish our papers in their journals. There doesn’t seem to be much momentum for the former, but the latter is already being done rather frequently. 

Across a variety of fields we see statisticians making direct contributions to science by publishing in non-statistics journals. Some examples are this recent paper in Nature Genetics or a paper I published a few years ago in the Journal of the American Medical Association. I think there are two key features that these papers (and many others like them) have in common:

  • There was an important scientific question addressed. The first paper investigates variability of methylated regions of the genome and its relation to cancer tissue and the second paper addresses the problem of whether ambient coarse particles have an acute health effect. In both cases, scientists in the respective substantive areas were interested in the problem and so it was natural to publish the “answer” in their journals. 
  • The problem was well-suited to be addressed by statisticians. Both papers involved large and complex datasets for which training in data analysis and statistics was important. In the analysis of coarse particles and hospitalizations, we used a national database of air pollution concentrations and obtained health status data from Medicare. Linking these two databases together and conducting the analysis required enormous computational effort and statistical sophistication. While I doubt we were the only people who could have done that analysis, we were very well-positioned to do so. 

So when statisticians are confronted by a scientific problems that are both (1) important and (2) well-suited for statisticians, what should we do? My feeling is we should skip the applied statistics journals and bring the message straight to the people who want/need to hear it.

There are two problems that come to mind immediately. First, sometimes the paper ends up being so statistically technical that a scientific journal won’t accept it. And of course, in academia, there is the sticky problem of how do you get promoted in a statistics department when your CV is filled with papers in non-statistics journals. This entry is already long enough so I’ll address these issues in a future post.

Related Posts: Rafa on “Where are the Case Studies?” and “Authorship Conventions”