Pro-tips for graduate students (Part 4)

This is part of the ongoing series of pro tips for graduate students, check out parts onetwo and three for the original installments. 

  1. You can never underestimate how little your audience knows/cares about what you are talking about (so be clear and start with the “why”).
  2. Perfect is the enemy of good (so do something good and perfect it later).
  3. Learn about as many different areas as you can. You have to focus on one problem to get a Ph.D. (your dissertation) but the best way to get new ideas is to talk to people in areas with different problems than you have. This is the source of many of the “Big Impact” papers. Resources for talking about new ideas ranked according to formality: seminar, working groups, meeting with faculty/other students, going for a beer with some friends.
  4. Here are some ways to come up with a new method: (i) create a new method for a new data type, (ii) adapt an old/useful method to a new data type, (iii) an overlooked problem, (iv) changing the assumptions of a current method, and (v) generalizing a known method. Any can be impactful, but the highest probability of high impact in my experience is (ii). 

Sunday data/statistics link roundup (6/24)

  1. We’ve got a new domain! You can still follow us on tumblr or here:
  2. A cool article on MIT’s annual sports statistics conference (via @storeylab). I love how the guy they chose to highlight created what I would consider a pretty simple visualization with known tools - but it turns out it is potentially a really new way of evaluating the shooting range of basketball players. This is my favorite kind of creativity in statistics.
  3. This is an interesting article calling higher education a “credentials cartel”. I don’t know if I’d go quite that far; there are a lot of really good reasons for higher education institutions beyond credentialing like research, putting smart students together in classes and dorms, broadening experiences etc. But I still think there is room for a smart group of statisticians/computer scientists to solve the credentialing problem on a big scale and have a huge impact on the education industry. 
  4. Check out John Cook’s conjecture on statistical methods that get used: “The probability of a method being used drops by at least a factor of 2 for every parameter that has to be determined by trial-and-error.” I’m with you. I wonder if there is a corollary related to how easy the documentation is to read? 
  5. If you haven’t read Roger’s post on Statistics and the Science Club, I consider it a must-read for anyone who is affiliated with a statistics/biostatistics department. We’ve had feedback by email/on twitter from other folks who are moving toward a more science oriented statistical culture. We’d love to hear from more folks with this same attitude/inclination/approach.