What my Undergraduate Research experience was like in Statistics

I am entering my third and final year of my undergraduate degree.  I have been doing research since almost day 1, and I wanted to share what my experience was like. As a statistician, I feel like I have to mention this is from a sample size of 1 and may not reflect all undergraduate research experiences.

First, I want to give a little background.  The summer before my senior year of high school, I was chosen to participate in an NSF (National Science Foundation) funded REU (Research Experience for Undergraduates)  at Texas Tech.  There I was exposed to what research was like.  We had a series of workshops each led by different researchers over a two week period. I loved the Texas Tech math department and decided to attend Texas Tech for my undergraduate degree. I meet my current research advisor Dr. Ellingson at the REU.

Right after classes started during my freshman year, I decided to email Dr. Ellingson and see if could do research with him.  I started work on image analysis (Dr. Ellingson’s specialty).  I was also following the GOP nomination because it was interesting to me.  I had an idea to predict the nomination using Bayesian statistics, similar to how Five Thirty Eight predicts elections.  I had talked with Dr. Ellingson about political science statistics before and how there was a need for a statistically sound open source academic model.  He agreed to help guide me through the process of building a model to predict the GOP nomination process.

At the time of the GOP nomination my math background was pretty limited, so I decided to just use Baye’s theorem and used the normal distribution to estimate likelihood.  I did all the calculations in excel and I downloaded csv files from Huffington Post Pollster with the poll data.  I used previous voting results from similar states as the prior in my model.  More info about my model can be found here. What I found the most challenging was making a lot decisions about how I was going to predict the election.  I also struggled with making the decisions about the delegate assignments which often involved breaking the results down by congressional districts, even when the poll data was state wide.  After the first Super Tuesday (March 1st) I began to realize that how difficult it is to find a good prior state and reassign support of candidates who dropped out of the race.  The nomination process taught me that failure is inevitable in research, especially in statistics, where everything is at least slightly uncertain.

In the summer of 2016, I started gearing up for the general election. I decided to use Scipy (a python package for science and stats) to make my predictions.  Making the programs was incredibly difficult.  I had over a dozen variations to match different combinations of poll data.  I had the programs up and running by early October, but I discovered a couple of bugs that invalidated my early test predictions.  The original plan was to run the model on the swing states two or three times before the real election. In the middle of October I discovered a bug in one of my programs.  I had to then fix the bug in every program.  I then finally did some manual calculations to confirm the programs worked.  It was difficult to have to admit that my early predictions were totally off, but I am glad I found it before the election.  Research isn’t like a homework assignment with answers in a solution manual.  You don’t know what is exactly going to happen and it is easy to make mistakes.

I ended up writing a paper on my 2016 general election model.  Writing an paper on your research is very different than writing a paper on other peoples research.  My paper was 14 pages (and over 6500 words) long, and only about one or two pages were about what other people’s research on the topic.  It took a very long time to write, and I had 17 drafts.  I hated writing the paper at first, but when I finished it felt amazing. It was definitely worth the effort.

Undergraduate research is difficult, but I loved the entire process.  I got to work with real data to solve a real problem.  I learned how to read a research paper, and eventually I got to write my own.  I got to give presentations to both general audiences and mathematicians and statisticians.  I got to use my research to  inform others about statistics. If you are thinking about doing undergraduate research, you definitely should.