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). 

Pro Tips for Grad Students in Statistics/Biostatistics (Part 2)

This is the second in my series on pro tips for graduate students in statistics/biostatistics. For more tips, see part 1

  1. Meet with seminar speakers. When you go on the job market face recognition is priceless. I met Scott Zeger at UW when I was a student. When I came for an interview I already knew him (and Ingo, and Rafa, and ….). An even better idea…ask a question during the seminar.
  2. Be a finisher. The key to getting a Ph.D. (other than passing your quals) is the ability to sit down and just power through and get it done. This means sometimes you will have to work late or on a weekend. The people who are the most successful in grad school are the people that just nd a way to get it done. If it was easy…anyone would do it.
  3. Work on problems you genuinely enjoy thinking about/are
    passionate about. A lot of statistics (and science) is long periods of concentrated effort with no guarantee of success at the end. To be a really good statistician requires a lot of patience and effort. It is a lot easier to work hard on something you like or feel strongly about.
More to come soon.

Characteristics of my favorite statistics talks

I’ve been going to/giving statistics talks for a few years now. I think everyone in our field has an opinion on the best structure/content/delivery of a talk. I am one of those people that has a pretty specific idea of what makes an amazing talk. Here are a few of the things I think are key, I try to do them and have learned many of these things from other people who I’ve seen speak. I’d love to hear what other people think. 

Structure

  1. I don’t like outline slides. I think they take up space but don’t add to most talks. Instead I love it when talks start with a specific, concrete, unsolved problem. In my favorite talks, this problem is usually scientific/applied. Although I have also seen great theoretical talks where a person starts with a key and unsolved theoretical problem. 
  2. I like it when the statistical model is defined to solve the problem in the beginning, so it is easy to see the connection between the model and the purpose of the model. 
  3. I love it when talks end by showing how they solved the problem they described at the very beginning of the talk. 

Content

  1. I like it when people assume I’m pretty ignorant about their problem (I usually am) and explain everything in very simple language. I think some people worry about their research looking too trivial. I have almost never come away from a talk thinking that, but I frequently leave talks confused because the background material wasn’t clear. 
  2. I like it when talks cover enough technical detail so I can follow the basic algorithm, but not so much that I get lost in notation. I also struggle when talks go off on tangents, describing too many subproblems, rather than focusing on the main problem in the talk and just mentioning subproblems succinctly. 
  3. I like it when proposed methods are compared to the obvious straw man and one legitimate competitor (if it exists) on a realistic simulation/data set where the answer is known. 
  4. I love it when people give talks on work that isn’t totally finished. This type of talk is scary for two reasons: (1) you can be scooped and (2) you might not have all the answers. But I find that unfinished work leads to way more discussion/ideas than a talk about work that has been published and is “complete”. 

Delivery

  1. I like it when a talk runs short. I have never been disappointed when a talk ended 10-15 min early. On the other hand, when a talk is long, I almost always lose focus and don’t follow the last part. I’d love it if we moved to 30 minute seminars with more questions. 
  2. I like it when speakers have prepared their slides and they have a clear flow and don’t get bogged down in transitions. For this reason, I don’t mind it when people give the same talk a bunch of places. I usually find that the talk is very polished.