## Pro-tips for graduate students (Part 3)

This is part of the ongoing series of pro tips for graduate students, check out parts one and two for the original installments.  Learn how to write papers in a very clear and simple style. Whenever you can write in plain English, skip jargon as much as possible, and make the approach you are using understandable and clear. This can (sometimes) make it harder to get your papers into journals.

## When dealing with poop, it's best to just get your hands dirty

I’m a relatively new dad. Before the kid we affectionately call the “tiny tornado” (TT) came into my life, I had relatively little experience dealing with babies and all the fluids they emit. So admittedly, I was a little squeamish dealing with the poopy explosions the TT would create. Inevitably, things would get much more messy than they had to be while I was being too delicate with the issue. It took me an embarrassingly long time for an educated man, but I finally realized you just have to get in there and change the thing even if it is messy, then wash your hands after.

## Pro Tips for Grad Students in Statistics/Biostatistics (Part 1)

I just finished teaching a Ph.D. level applied statistical methods course here at Hopkins. As part of the course, I gave one “pro-tip” a day; something I wish I had learned in graduate school that has helped me in becoming a practicing applied statistician. Here are the first three, more to come soon.  A major component of being a researcher is knowing what’s going on in the research community.

## "How do we evaluate statisticians working in genomics? Why don't they publish in stats journals?" Here is my answer

During the past couple of years I have been asked these questions by several department chairs and other senior statisticians interested in hiring or promoting faculty working in genomics. The main difficulty stems from the fact that we (statisticians working in genomics) publish in journals outside the mainstream statistical journals. This can be a problem during evaluation because a quick-and-dirty approach to evaluating an academic statistician is to count papers in the Annals of Statistics, JASA, JRSS and Biometrics.

## Reverse scooping

I would like to define a new term: reverse scooping is when someone publishes your idea after you, and doesn’t cite you. It has happened to me a few times. What does one do? I usually send a polite message to the authors with a link to my related paper(s). These emails are usually ignored, but not always. Most times I don’t think it is malicious though. In fact, I almost reverse scooped a colleague recently.

## Show 'em the data!

In a previouspostI argued that students entering college should be shown job prospect by major data. This week I found out the American Bar Association might make it a requirement for law school accreditation. Hat tip to Willmai Rivera.

## Preparing for tenure track job interviews

If you are in the job market you will soon be receiving (or already received) an invitation for an interview. So how should you prepare? You have two goals. The first is to make a good impression. Here are some tips: 1) During your talk, do NOT go over your allotted time. Practice your talk at least twice. Both times in front of a live audiences that asks questions. 2) Know you audience.

## Expected Salary by Major

In thisrecent editorialabout the Occupy Wall Street movement, Richard Kim profiles a protestor that despite having a master’s degree can’t find a job. This particular protestorquit his job as a school teacher three years ago and took out a \$35K student loan to obtain a master’s degree in puppetry from the University of Connecticut. I wonder if, before taking his money, UConn showed this person data on job prospects for their puppetry graduates.

## The self-assessment trap

Several months ago I was sitting next to my colleague Ben Langmead at the Genome Informatics meeting. Various talks were presented on short read alignments and every single performance table showed the speaker’s method as #1 and Ben’s Bowtie as #2 among a crowded field of lesser methods. It was fun to make fun of Ben for getting beat every time, but the reality was that all I could conclude was that Bowtie was best and speakers were falling into the the self-assessment trap: each speaker had tweaked the assessment to make their method look best.

## Finding good collaborators

The job of the statistician is almost entirely about collaboration. Sure, there’s theoretical work that we can do by ourselves, but most of the impact that we have on science comes from our work with scientists in other fields. Collaboration is also what makes the field of statistics so much fun. So one question I get a lot from people is “how do you find good collaborations”? Or, put another way, how do you find good collaborators?

## Authorship conventions

The main role of academics is the creation of knowledge. In science, publications are the main venue by which we share our accomplishments, our ideas. Not surprisingly, publications are heavily weighted in hires and promotions. But with multiple author papers how do we know how much each author contributed? Here are some related links from Science and Nature and below I share some thoughts specific to Applied Statistics. It is common for theoretical statisticians to publish solo papers.

## Single Screen Productivity

Here’s a claim for which I have absolutely no data: I believe I am more productive with a smaller screen/monitor. I have a 13” MacBook Air that I occasionally hook up to a 21-inch external monitor. Sometimes, when I want to read a document I’ll hook up the external monitor so that I can see a whole page at a time. Other times, when I’m using R, I’ll have the graphics window on the external and then the R console and Emacs on the main screen.

## Finish and publish

Roger pointed us to this Amstat news profile of statisticians including one on Francesca Dominici. Francesca has used her statistics skills to become a top environmental scientist. She had this advice for young [academic] statisticians: First, I would say find a good mentor in or outside the department. Prioritize, manage your time, and identify the projects you would like to lead. Focus the most productive time of day on those projects.

## Statistician Profiles

Just in case you forgot to renew your subscription to Amstat News, there’s a nice little profile of statisticians (including my good colleague Francesca Dominici) in the latest issue explaining how they ended up where they are. I remember a few years ago I was at a dinner for our MPH program and the director at the time, Ron Brookmeyer, told all the students to ask the faculty how they ended up in public health.

## Meetings

In this TED talk Jason Fried explains why work doesn’t happen at work. He describes the evils of meetings. Meetings are particularly disruptive for applied statisticians, especially for those of us that hack data files, explore data for systematic errors, get inspiration from visual inspection, and thoroughly test our code. Why? Before I become productive I go through a ramp-up/boot-up stage. Scripts need to be found, data loaded into memory, and most importantly, my brains needs to re-familiarize itself with the data and the essence of the problem at hand.

## Another academic job market option: liberal arts colleges

Liberal arts colleges are option that falls close to the 75% hard/25% soft option described by Rafa in his advice for folks on the job market. At these schools the teaching load may be even a little heavier than schools like Berkeley/Duke; the students will usually be exclusively undergraduates. Examples of this kind of place are Pomona College, Carleton College, Grinnell College, etc. The teaching load is the focus at places like this, but research plays an increasingly major role for academic faculty.