Issue #17: Why I chose Biostatistics

The origin story

In this issue, I’m going to talk about how and why I got into Biostatistics. I didn’t start out as a math or statistic major, and neither of my parents know anything about statistics. How I got here is somewhat of a fluke, but I’m happy that it happened. I don’t talk a lot about myself aside from remembering statistical examples, so I’m giving it a shot.

Jack of all trades, master of none

I went to UC Davis, and I majored in biomedical engineering. I remember choosing this major because it was a happy medium between what I thought I wanted — to make stuff – and what I thought my parents wanted: a stable degree. I liked my major because it had a little bit of all the different engineering disciplines. It was very much a jack of all trades type of major.

What I didn’t know was that this would work against me when I graduated. I couldn’t find a job. I knew a lot of stuff, but I only knew it superficially. I couldn’t compete with the people who majored strictly in electrical or mechanical engineering.

Desperate to get something related to my major, I took up a job as a “junior specialist” in a biomedical engineering lab. This was where I learned that bosses can either make your life great or a nightmare. I experienced the latter. A year later, I was out of there and back to struggling with searching for a job.

Finding programming and data science

Finding a second job was not so easy. I needed to pay rent, so I took up multiple jobs while I searched for another job adjacent to the medical field. Those were tough times, and I remember the disapproving looks from relatives when I told them I was a server. That sucked.

My current resume wasn’t getting me many interviews, so I started to pick up new skills here and there. Most of these didn’t amount to much, but one did: Python. Back in my undergrad, the first programming language that I learned was Matlab. Gross.

But through Python, I realized I kind of liked programming! More specifically, I was really into data science, a new term that was popping up around then. The hype around data science was intense, and I wanted to be a part of it. I got comfortable enough with Python to talk about how I would work through problems, and this ability helped land me my second major job: a data analyst for the UC Davis Medical Center. I did analyses for a doctor studying hospital-borne infections.

Little did I know that this position would be a turning point in my life.

Fateful encounter

As an analyst, I didn’t really know how to do much on my own. I took two statistics classes as an undergrad, and I got a C in the latter. But what helped me was that my boss was very supportive. He knew enough stats to tell me what procedure was required, and it was up to me to learn it to perform it. He trusted me to learn what he needed.

But, one day, he didn’t know what to do with a dataset. And I sure as hell didn’t know, so he did something new. We needed to meet with someone to figure it out. A… biostatistician?

At that time in my life, MDs and Ph.Ds seemed to be the pinnacle of intelligence. If anyone could figure out an intellectual problem, it was these people. In retrospect, this is silly, but no one in my life has either of these, so I just kind of assumed that’s how it was. The fact that my boss needed help was crazy to me, so I was very curious to meet this “biostatistician”.

Before the meeting, I remember waiting in a busy office. It seemed that my boss and I weren’t the only appointments. Bored, I looked at everyone’s name tags (required to walk around a hospital building), and I realized they were all doctors as well. They were all meeting with the biostatistician?

My boss described his problem, and without skipping a beat, the biostatistician knew what to do. We had only met this person, and they knew our dataset and solution better than us. Not only that, but she told me how to do the analysis in a way that I understood.

That was the moment I decided to go into biostatistics.

That’s when I realized that statistical ability is one of those skills that will always be necessary, no matter what field you’re in. Researchers are always developing new ideas, and by extension, they’re always generating data. So long as data is being made, statistics soon follows.

A month later, I sent a half-serious application to a few Master’s programs in Biostatistics, and in the fall, I was in New York taking my first graduate class.

I don’t remember this statistician’s name, and I doubt they remember me. But I will never forget the impact they had on me that day.

Thanks for listening, I’ll see you all next week.

Christian

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Current State of The Channel

😵‍💫 What am I working on right now?

• I’m working on an explainer video on some basic nonparametric tests

🧐 What am I enjoying right now?

• Book — I finished listening to So Good They Can’t Ignore You. I unearthed an old strategy of Cal’s dealing with learning mathematical proofs. It comes at a perfect time, I think it’s time I try to tackle one for my own research.

• Thing — I got a camera! You’ll be seeing my face a lot more, and less stock footage.

📺 What are my recent videos?

• 5 tips for getting better at statistics: a video detailing some of my best practices for learning statistics, honed from struggling in graduate school

📦 My other stuff

• I wrote guided solutions to problems to Andrew Gelman’s Bayesian Data Analysis. It’s for advanced self-learners teaching themselves Bayesian statistics

Heads up! Some of the links here are affiliate links, so I may get a small amount of money if you buy something from them. I only link stuff I actually use.