Applying to Graduate School in Statistics and Biostatistics

There were all sorts of tips and tricks about applying to grad school that I was only able to learn about via office hours, personal meetings, and Twitter DMs, and I thought it would be worth publicly compiling some lessons learned.

Published

March 15, 2021

Since last fall, I’ve been going through the process of applying to graduate school in (bio)statistics.1 I found that I was only able to learn about some parts of the process through office hours, personal meetings, and Twitter DMs, and I thought it would be worth publicly compiling some lessons learned.2🐥 I’m far from an expert about how this all works, and can only speak to my personal experience.

A few things to note that influenced my personal experience: I’m a cis white man with U.S. citizenship who is an alumni (soon-to-be) of a private U.S. liberal arts college majoring in math with a concentration in statistics. I ultimately decided to apply to Ph.D. programs in Biostatistics in the U.S. during fall 2020. When I started my undergrad, I didn’t know what a Ph.D. was and had little—if any—sense for what graduate school looked like. However, by the time I was starting to think about starting my applications, I had learned a good bit more about what graduate school was. My test scores and GPA were quite unimpressive, but I’d been lucky enough to gain quite a bit of experience in statistical research and software development and had strong recommendation letters.

The order of this post roughly follows the order in which I asked myself questions about graduate school in (bio)statistics.

Many of these answers speak to Ph.D. programs more so than M.A./M.S.’, and some may apply more accurately to biostatistics than statistics. I don’t have a good understanding of how many of these answers apply to schools outside of the U.S., and many of these answers depend on my lived experience in some other way.3 I’ll try to specify when I understand that to be the case.

I’ve tried to be as forthcoming as possible while writing this up, as I’m not sure it helps anyone to keep so much of this information behind closed doors. I apologize if I’m unnecessarily frank.

What is graduate school in (bio)statistics?

This was the most difficult question for me to answer and the question that resulted in the most people looking at me like I had just grown a second head when I asked it.

A reality of graduate school: many people who attend(ed) graduate school grew up around a lot of people who attended graduate school. That does not mean you need to have grown up around a lot of people who attended graduate school to go (or so I’m told—we’ll see). In many important ways, though, it doesn’t look like your time as an undergraduate.

“Graduate school,” at least in (bio)statistics, generally refers to Masters (M.A./M.S.) and Doctorate (Ph.D.) programs. Typically, you enter graduate school after receiving a Bachelors (B.A., undergraduate) degree, whether that’s directly after or after a few years of work experience. You can also apply to Ph.D. programs after having received an M.A./M.S. I’ll speak more to this in a bit, but M.A./M.S. programs typically take 1-2 years and present somewhat more like undergraduate programs. Ph.D. programs take longer—4-7 years—and look (and pay) a bit more like a job.

Should I go?

I recommend spending a good amount of time with this question, especially if you’re coming from an institutional setting where going to grad school feels like the “logical next step.” In some ways, it’s not.

The best first step to answering this question is learning a lot about what it means to attend grad school—for your finances, lifestyle, job prospects, and life timeline. I can speak somewhat to how these things could look if you do attend grad school, but how they might look if you don’t is more specific to you.

Things you will do a lot of during your time in grad school:

  • take classes in statistics (and possibly fields of specialization)
  • teach courses in statistics, but maybe also math
  • take part in research, including
    • meeting with lots of folks + talking science
    • writing math, code, papers
    • attending conferences
    • attending and giving talks
  • all of the above things, at once. You’ll work quite a bit.
  • not get rich

The relative importance of those first three bullets can depend quite a bit on whether you’re doing an M.A./M.S. or Ph.D. More on that in a sec.

If most of the things above get you pretty stoked, maybe it’s the right thing.🦆

How much does it cost?

This actually wasn’t one of my first questions, but it ought to have been. I assumed since grad school is, you know, school, you probably pay for it like undergrad. Sometimes (maybe often?) not—read on. :-)

(Update, 8/2/21: I wrote a bit about the NSF GRFP, a fellowship program that can pay you even more for pursuing a Ph.D. You can read that post here.)

Should I do an M.A./M.S. or Ph.D.?

Some assorted thoughts on how the two are different and alike:

  • M.A./M.S. programs are shorter (usually 1-2 years).
  • M.A./M.S. programs tend to look a bit more like undergraduate programs in that 1) you usually pay to attend them and 2) the majority of the experience revolves around taking classes. You might also do some research or teach.
  • The first year or two of a Ph.D. is mostly focused on coursework. The latter part is generally based around you carrying out research and teaching undergraduate courses, and lasts something like 2-5 years. This research culminates in a dissertation, which is… a big paper, often composed of research papers you published during your time in the program, and some change.
  • You get paid to do a Ph.D. Bonkers. Usually, salaries (“stipends”) range from $20-35k annually (depending, among other things, on the cost of living in the city), also covering the cost of tuition, and require that you do some sort of research or teaching along the way.💰 Don’t do a Ph.D. if you will not be financially supported by your department. Some departments also offer signing bonuses, cover moving expenses, and provide pools of money for conference travel. Note, though, that in many cases, academic departments will often require you to pay for travel up front and then reimburse you after the fact. It’s also my sense that academic departments do not have the sense of urgency to pay their students in the way that other employers do—you may be doing lots of “checking in” in order to receive the payment you were promised.
  • (Update, 8/2/21: I wrote a bit about the NSF GRFP, a fellowship program that can pay you even more for pursuing a Ph.D. You can read that post here.)
  • It seems like M.S. programs tend to offer a wider range of degree titles (like Data Science or Business Analytics) tailored to specific career goals. M.A. programs tend to look more like the first two years of a Ph.D. and are funded more often than M.S. programs.
  • It seems like Ph.D. programs allow you dive deeper into more specific concentrations in your latter years of the program.
  • You can apply to Ph.D. programs after graduating with an M.A./M.S.! Some M.A. programs will offer graduating students admission into their Ph.D. programs.
  • Generally, Ph.D. students seem to be treated better than M.A./M.S. students. Better access to office space, health insurance, faculty attention, campus amenities, etc.

I’m not sure how well this applies to programs outside of the U.S.

What’s the difference between statistics and biostatistics?

Of all of the questions that I try to speak to in this blog post, I feel like this answer might be the most unsatisfactory for people who really know “what’s up.” I think many in the statistical community could benefit from speaking and listening earnestly to how we delineate these fields.

I’ll list out a few of the main tendencies I’ve picked up on over the last year or two. In reality, these characteristics exist more so at the departmental level rather than at the “field” level, and you’ll see a lot of variation in how departments in either field position themselves relative to these traits.

  • Biostatistics departments are usually situated in public health schools, while statistics departments tend be situated in schools of arts and sciences with some relation to the mathematics department.
  • Many biostatistics departments seem to really value interdisciplinary research with collaborators from elsewhere in that school of public health. Statistics departments seem to be more self-sustaining in generating their research questions.
  • Biostatistics seems to focus more on application, while statistics seems to focus more on theory. You will surely take part in both in either kind of program, though.
  • Statistics departments seem to look to your math chops (however displayed) in admissions more so than biostatistics departments. Biostatistics departments seem to appreciate some non-math backgrounds more so than stat departments might, like software development or fields in public health. You’ll need math chops for either, though.

There are a few “applied statistics” programs out there as well. They tend to look somewhat more like biostatistics programs (omitting the first bullet point) yet draw from a very wide pool of disciplines in their collaborative work.

If any of these distinctions make you feel as if you’re particularly excited about one of biostatistics or statistics, I’d encourage you to rather look for programs that exhibit that trait rather than fall into the biostats/stats bin I mention above. These traits exist on spectra, and the biostats/stats feature here is only moderately predictive.4

What’s the deal with the GRE?

The GRE is something like the ACT/SAT of graduate admissions. In comparison to those tests, though, it’s more expensive and even less correlated with success in the program it’s supposed to test your preparation for. Nevertheless, it’s still a part of admissions for many programs, so it’s worth speaking to.5

The test will put you out 200 bucks or so, but fee waivers are available. They have some income/citizenship requirements, and decrease the cost of the test by 50 percent. Some undergraduate institutions have internal scholarships to help pay for taking the test, so that’s worth a look.🌞

There’s all sorts of advice out there about how to do well on the test, and I didn’t do well, so I won’t speak to that. A few stray notes about how the test is situated/regarded, though:

  • There’s a “general” test and a “subject” test. The general test feels more like the SAT/ACT, and is required by many more programs than the subject test. Generally, biostatistics programs don’t require the subject test. Some statistics programs do.
  • The general test is broken up into math, reading, and writing sections. The math and reading sections are graded on a scale from 130-170, and you receive a separate score for each. Apparently, these programs don’t care too much about your reading score. More emphasis is placed on the math score, though. Programs will typically mention some sort of distributional measures about their admitted students’ test scores on their admissions websites. The most competitive programs, if they require the GRE, tend to admit only students with near-perfect math sub-scores—think 166-70. 150s through mid-low 160s seem to be typical for less competitive programs.
  • I didn’t take the subject test, but my understanding is that it’s very hard to achieve a score that will put you ahead in admissions unless you have significant coursework in computation-based upper-level mathematics courses and/or are willing to put significant time in to studying for the test.
  • Some graduate schools use the GRE as a “filter.” At these schools, a score below some threshold means the committee may never put eyes on your application. I don’t have a good sense for how common this practice is.

Who’s to say whether grad programs will stick with this decision to omit the GRE as an application requirement once in-person standardized testing is available again. Props to those who do.

How many schools should I apply to?

There are a few things to think about here.

The biggest limiting factor for me was price—it’s about 100 bucks per application. Most schools provide fee waivers, which are a varying degree of 1) financially helpful and 2) a pain in the ass to apply for. Generally, you might need to be on a Pell grant to apply for a fee waiver, and the waiver will cover most, but not all, of the application fee. Also, ask professors/mentors/staff at your institution about possible pools of money which you may able to draw from in order to help cover these fees.

Another thing to keep in mind is how you think about your chances of getting into the schools you apply to.6 If you feel you have a strong application and the schools you’re applying to aren’t particularly competitive, you might decide to apply to fewer schools than you otherwise would, though I’d caution from leaning on this sort of thinking too heavily. From what I’ve seen, folks’ rate of admission to grad programs has been much less correlated with that schools’ ranking than I expected while applying.😕

Some sage advice I received that ultimately influenced me to decide to cut back on my number of applications: don’t apply anywhere that you don’t genuinely want to go to. If you’re not feeling excited about living in a city and working in a department for 4-7 years (or 1-2 for a Masters), be earnest with yourself—save your money and don’t apply.

Bottom line: I applied to seven schools and was rejected from most of them. I’ve heard of some folks applying to four or five, and some well into the teens. It seems like a typical number is seven to ten.🌿

How do I decide which schools to apply to?

I recommend starting with thinking about the things that are most important to you before thinking about specific programs. How important is location to you? The size of the school? Presence of faculty with a specific research interest? Prestige?7 Anything else?

Another big factor here is getting a sense for how competitive of an applicant you are. The answer to this might depend on the program, both in that some schools are harder to get into than others, and also that different programs look for different qualifications in their applicant pool. For instance, a statistics department with an emphasis on theory might really care about your performance in a real analysis course and GRE math scores, or a biostatistics department with an applied slant might be especially excited to see significant experience with statistical computing. In reality, most all school websites will say they look for—and offer—a good mix of both theory and application, and it’s hard to really know until you have a chance to talk with some folks in the department during an interview.

Some criterion, like ranking and location, are much easier to find information about than others, like finding faculty who do research you’re interested in or determining a program’s orientation toward international students.8 I thus found it easier to start with the information that’s easier to access to initially filter down to a list of schools that I could reasonably research about the harder-to-find information. That harder-to-find information, though, will likely be what really helps you know whether you want to apply to a given program.

In reality, you might not know exactly what it was you’re looking for in a program. That was the case for me. This is what my process looked like:

  1. Scan through the U.S. News Rankings to get a sense for what all is out there.9
  2. Choose 7-12 schools that are 1) in places you’re down to live in and 2) distributed somewhat uniformly across the rankings you think you have a chance at. For example, I really didn’t have a good sense for how competitive my application was, so my initial list was something like 1 school per “tens place” (e.g. one from the single digits, one from the tens, twenties, and so on through the eighties).
  3. Pull up the websites for each of those schools. Read about the faculty and the town/city. Most departments have a faculty page that lists names and a few research interests—check out the personal websites of any professors you think might be cool to work with. Write down the names of professors whose work you found interesting and general research areas that seem to pop up a lot in their profiles.
  4. For each school, ask yourself “Am I genuinely excited about this place?” If so, keep it on your list. If not, scratch it off.
  5. While you were checking these places out, did you learn anything about what you’re looking for in a program? Write down common characteristics of the schools you were interested in. If so, keep that in mind while you…
  6. Bulk your list back up to 10 or so schools again with U.S. News—and your new criteria, if any—trying again to keep somewhat of a balance in terms of really highly-ranked schools and not-so-highly-ranked schools.
  7. Iterate on steps 3-6, giving yourself a day or two between iterations to stew a bit, until you land on a list of schools that you feel excited about possibly spending a few years attending.

A stray note—I wasn’t interested in joining departments that continue to value the GRE as an informative criterion for persistence/success in the program, despite the evidence to the contrary as well as its deleterious effects for diversity of class and race. It felt to me like a window into departmental culture. So that was another way I whittled down my options.🗜

What does a grad school application look like?

A few of the common elements of these applications, binned by how important they seem to be:

  • Very important:
    • solid letters of recommendation
    • one or more of: research,10 internship/work, or software development experience, or some other “selling point”
  • Important:
    • thoughtful personal/research statement11
    • thoughtful diversity statement, if applicable
    • solid grades in key courses
    • a lack of a negative internet presence
  • Good to have:
    • solid grades in courses early on in undergrad
    • positive internet presence

Again, the relative importance of each of these pieces will vary quite a bit depending on the program of interest, and I may be flat out wrong in some of my generalizations here.

A solid letter of recommendation is from a professor or research mentor who knows you well and can speak to your specific strengths, ideally at length. Ideally, they have a terminal degree in their field (e.g. a Ph.D. in (bio)stat, math, etc.). Usually, programs will ask for three letters—ideally, at least one of those will be from a (bio)statistics professor or practitioner. If you’re applying to stat programs, one of these probably ought to be from a math professor, ideally your professor for Real Analysis, if you’ve taken it. If you’re applying to biostat programs, one of these probably ought to be from a research or internship mentor. You should keep your recommenders in the loop on how your application is coming together and where you’re applying (including the application deadlines for those programs).

There a few things I’m thinking about when I say “research, internship, work, or software development experience, or some other ‘selling point’.” For one, having done one of these things means you know what you’re getting into beyond coursework. If you’ve gotten a feel for any of these things, you’ll have a bit better sense for what grad school could be like. Also, having done one of these things likely means that you had a supervisor or collaborator that you worked closely with that can write you a strong and specific letter of recommendation. Lastly, having done one or more of these things will help you articulate your “story.”

When I say “story,” I’m mostly thinking of the personal statement. Your personal statement gives you a chance to explain how it is that you became interested in grad school and how your previous experiences show that you will succeed there. There’s lot of advice out there about how to write a personal statement, and most of the prompts you’ll come across are very similar, so I won’t speak to this too much. Rohan Alexander12 recently wrote a really thoughtful blog post that spends more time with what a personal statement ought to look like (as well as some good notes on how to think about the role of recommenders).

One thing, though—I think some are hesitant to be publicly forthcoming in their workflow on personal statements. The private advice I’ve been given on how much time + effort to put into a personal statement has often tended to recommend much less care than that from advice I’ve read online. So, to be frank—personally, most of my personal statement was generic and sent to every program I applied to. For each program, I wrote a few sentences on why I was specifically interested in it, and pushed surrounding sentences from the generic document around as needed so that the program-specific statements flowed naturally. I also did Find + Replace for the program name and type (biostat vs. stat) in a couple places. Before submitting each to the official portal, I read the document in its entirety. I spent a weekend total on writing my personal statements, and had my two roommates give a round of edits. This does not include the time spent learning the information about programs I ultimately drew from in writing my statements.

I mention “positive” and “negative” internet presence above. I’m generally thinking about what might come up if I look up your name with a search engine. Positive kinds of presence could be a personal website, LinkedIn, blog, professional Twitter, et cetera. If you’re an R user, no one writes better learning materials related to getting a personal website or blog started than Alison Hill. Check out her resources on blogdown if you’re interested! Negative kinds of presence are the typical social media goofballery you’ve probably been warned to be wary of participating in—just give a thoughtful eye to your privacy settings.🙂

Regarding internet presence, there are many reasons why one may not want to be discoverable on the internet. Many of these relate to violence and harassment committed by cis men via the internet. Cis men, cut that shit out. My DMs are open if you want to talk with another cis man about being a more welcoming presence online—I’ll always have more to learn here as well.

What should I spend the most time on while applying?

In my opinion, you ought to spend most of your time thinking about why it is that you want to go to grad school and finding programs that you believe will be able to offer that thing that you want. The process will be much, much easier easier if you’re genuinely stoked about the thought of ending up where you’re applying.

Once you’ve decided where you want to apply, here’s some sage advice from Rohan’s blog post linked above: “There are three bits to the application—transcript, letters, statement/CV. You’re spending four years getting transcripts, so you should also spend a lot of time on the other two.” You’ll likely need to spend a good amount of time putting together a thoughtful application. That said, grad school applications will take up as much time as you let them. This is one of the few spots in this post where I think it might be uncool to suggest specific “amounts of” something (time or effort, here) to spend applying, especially because I have no idea how long it took me and this will vary a lot from person to person.

I will say that some of my most productive moments in figuring out what I wanted from grad school—and how to reflect that in my application—happened away from my computer. If it’s an option for you timeline- and mental health-wise, take a couple days away from your applications frequently.

Should I reach out to potential advisers? Or mention their name(s) in my application?

After some discussions, an update to this section of this post, and further discussions on this section, I’m not sure I can speak confidently to this question.

My best advice here is to:

  1. Read professors’ Contact pages thoroughly to see if they appreciate/expect being reached out to.
  2. Ask your recommenders if they have thoughts about this practice, especially as related to the specific departments you’re applying to.

:-)

I’ve collapsed the previous iteration of this answer below!

It seems like the norm in some academic fields is to have specific advisers that you’d like to work with in mind before applying. In some, even, you might be expected to reach out to those advisers and have informational interviews before applying.

My sense is that this is generally not an expectation in (bio)statistics. Smaller schools may appreciate if you show that you know what some number of specific faculty do, but in general, do not feel like you need to reach out to specific faculty or even mention them in your application.

You should mention specific faculty in your application if there are people you’re really interested in working with (and especially if you really only want to go to a school if you’re able to work with a specific adviser). If you do so, expect that a school will take that seriously to some extent. For example, if you are given an interview with a school, that person might be your interviewer. If not, and you are accepted, that person might reach out to you personally following your acceptance. (My n here is really small.) If you mention a really prominent faculty member in your application, show that you know how their research interests relate to others’ in the department, and maybe mention another more junior professor or two.

Only send faculty an email while you’re applying if all of the following are true:

  1. You really want to work with them
  2. You’ve checked their website to see if they mention their boundaries re: being reached out to
  3. You can’t find any signs that they are surely not taking students—if this is unclear from what you can find online, asking whether they’re taking students could be part of your introductory email!

John Muschelli at Johns Hopkins Biostatistics gave some great advice related to what that introductory email could look like in a recent blog post.

Regardless of whether you mention the names of specific faculty members, your application should reflect that you do have a sense for what faculty in the department research and that you have demonstrated interest in those research areas. Demonstrated interest could be prior research, but also speaking insightfully to the research area and its implications.

What do I do after I apply?

You’ll likely wait a good while. Some programs list dates you’ll hear back by on their website, and some subset of those dates are correct/up-to-date. The latest date for most offers is April 15th, though offers start coming out as early as January. Biostatistics programs seem to have earlier application deadlines and get back to people earlier.🕰

There are online forums that exist where people post their admissions results and share their anxieties about the process. I peeked at them a bit, and ultimately found that, more than anything else, they just made me more anxious. For that reason, I won’t link them specifically here, but they exist, and you can find them if you so please.

Depending on the program, you may be accepted outright via an email, or you may go through some rounds of interviews beforehand, or maybe even flown out to their campus to visit. Since I applied during the COVID-19 pandemic, my experience looked very, very different than yours probably will. John Muschelli wrote up some really thorough notes, though, on the post-application part of the grad admissions process, and he’s a wicked smart person. So, maybe, check his writing out.🌚

His sage advice from that post regarding this part of the process that I want to shout from the rooftops is this:

Asking for things is not only OK, but is expected.

Especially once you’re in, or even interviewing, ask away. I’ve often felt like a nuisance with my questions about what my life will be like here soon, but once my wonderful advisor13 manages to convince me to send out the email, I’m always pleasantly surprised at how genuinely helpful and forthcoming interviewers, professors, and staff are.

Some Final Thoughts

Many blog posts in this genre have a common ending, and for good reason. It goes something like this: be good to yourself. There’s a lot of luck and randomness involved in this process—the more you can separate your self-worth from the results of your applications, the better.🐛

Other people have spoken earnestly and helpfully to navigating this process; this is surely not the first or last post in this genre. Here are some links to other wonderful blog posts on similar topics, some of which I’ve cited already:

  • Rohan Alexander on your CV, personal statement, and rec letters
  • John Muschelli on applying, interviewing, choosing between offers, early grad school life
  • Amelia McNamara in a Q&A style post, including many, many links to helpful information and perspectives

I’m not sure I’m comfortable sharing my application materials publicly. However, please feel free to shoot me an email if you’re going through this process and would appreciate giving a glance at the documents I ended up putting together. Mostly thinking of personal statement and CV here.

Also, if you identify as a person holding an underrepresented identity in (bio)statistics, I’d be glad to give some or all of your materials a read over and answer any other questions about the process you may have. Just give me a holler, no need to speak to the identities in question if you don’t want to.

I’m generally, like, not a popular blogger, or whatever, so I’m not too worried on being overloaded with requests here… so please feel free to ask away! I’ll update this text if this changes.

Regardless, if this post has been helpful for you, give me a holler. It’ll make me smile.🌸`

Thanks to Grayson White (@graysonwwhite) for his feedback on this post. Incredibly helpful.

Edit, 25 March 2021: Added some additional thoughts thanks to enlightening conversations with Keshav Motwani (@keshav_motwani).

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Footnotes

  1. Throughout this blog post, (bio)statistics refers to something like “statistics and/or biostatistics.”🐳↩︎

  2. If you were one of those people with whom I met to help me navigate this process, thank you. I’m better for knowing you.↩︎

  3. Namely, in being a U.S. Citizen. While the mechanisms by which this takes place remain opaque to me, it seems to be commonly understood that ones experience with the U.S. graduate school admissions process is heavily influenced by U.S. citizenship. Which is fucked.↩︎

  4. I’d appreciate links to any blog posts or talks you’ve found helpful clarifying this distinction that I could link out to. PRs, tweets, whatever! Thanks to Paul Nguyen for passing along this one from Michael Lopez.↩︎

  5. I applied to graduate school during the COVID-19 epidemic, during which many schools waived their requirement for the GRE. Hopefully, the omission of these tests in admissions decisions is a more lasting phenomenon, but… power. Damn.↩︎

  6. You may be thinking of a distribution here. It might be the poisson-binomial. Have at it.😉↩︎

  7. Prestige likely ought to be a factor if you’re applying to Ph.D. programs and your goal is to ultimately teach at the university level, especially at big, research-focused universities. This may not be as important if your goal is to ultimately enter the workforce following graduation.↩︎

  8. The latter of which is fucked.↩︎

  9. Lots to say here, for sure. I take issue with what a lot of these rankings represent and reinforce re: status obsession among the rich and/or well-educated, but I’d rather be up-front here. The U.S. News Rankings were my go-to for getting a sense for what programs were out there and, roughly, how they were regarded, and the same goes for others with whom I talked who were going through the same process.↩︎

  10. Including, but not limited to, published papers.↩︎

  11. Applications seemed to refer to this essay type by one of “Personal Statement” or “Research Statement.” The associated prompts didn’t seem to differ too much based on which word they used.↩︎

  12. Awesome person.☃️↩︎

  13. Hi, Kelly. Thank you.↩︎

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