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I think you'll get into many top 10 stats PhD programs and probably any biostats PhD program. I seriously wouldn't be worried about getting in to a good program if I were you. Since you'll probably get in pretty much anywhere, try to focus more on program fit. Location, potential advisors, research areas, funding, curriculum, etc. are all important. Not all statistics PhD programs will be extremely theoretical, although most will require a similar degree of theoretical core coursework. If you like bio applications, a biostats program may be a good place for you, but many stats programs have heavy research in bio applications as well. Just depends on the program.

I would pick the undergrad thesis advisor and probably the lecturer you've had multiple courses with, assuming you have a good relationship with them. Very few undergrads have done any meaningful statistical research, so I don't think an admissions committee would expect that of you. Your undergrad advisor will be able to attest to your general research ability and potential though, which is definitely important. It also sounds like you have a good relationship with them and have done some good work, so it's a no-brainer to me to pick your thesis advisor. Between the lecturers, it probably doesn't matter much which one you pick as long as you've developed a relationship with them and they can attest to your good work and ability in the classroom.

Looks like you have great grades, test scores, and depth of mathematical coursework along with some research experience with solid LoR's. I think you would be competitive pretty much everywhere for PhD programs, just depends on what you want (location/research area/etc.). Any ideas what kind of research you want to do, or do you have any geographical preferences?

If you're at strong university known for grade deflation, I wouldn't be too concerned about the B's. A C in Real Analysis I might raise some eyebrows though, but I could be wrong. What are these "more difficult" classes you mentioned? I hope ranking isn't your #1 factor when choosing which program to attend. Keep in mind you'll be in that department working with those people for 4-5+ years, so you need to make sure you also weigh other factors that affect your day-to-day life and happiness. On another note, I can't even fathom paying 50k/year for an undergraduate degree. There are a lot of solid programs outside of the top 5 though, so you definitely shouldn't feel bad if you end up finding a lower ranked program that is a much better fit in other aspects (people, research areas, location, funding, etc.) It sounds like you're doing a lot this summer and in the fall that will greatly strengthen your application. In addition to those things, I would really focus on building solid relationships with your professors. Go to office hours, show genuine interest in their research, ask good questions. I didn't decide that I wanted to do a PhD until around the same time as you (around spring of my junior year), and I think one of the things that really strengthened my application even though I didn't have a super deep math background or any publications was that I had great relationships with some professors who wrote me very solid letters of recommendation. If you want more specific feedback about which programs may fit your profile, I suggest posting some more details (like specific courses and grades, relevant previous research or other experience, research and location preferences, etc.)

I think you'd be competitive at any of those schools since you have solid grades and a good depth of math coursework from a good school. Usually you need 3 LOR's, and I think having at least one from a professor who can attest to your research potential would be wise.

One big reason I chose the program that I am currently attending is because the majority of PhD grads from my department go into industry and have great placements. My goal has pretty much been industry from the beginning, and the same is true for probably about half of my cohort. Biostatistics PhD programs tend to be more applied than Statistics programs, so as long as you have the basics (like calc 1-3, linear algebra, real analysis), you shouldn't worry too much about taking a lot of additional advanced math classes just to have them on your transcript. A lot of this is program dependent though, so just make sure you understand the level of mathematical rigor at each program you're looking at applying to by reading through the list of required courses. Keep in mind though that having a deeper math background can go a long way in putting yourself ahead of other applicants.

I don't know about NC State's program, but many Stats PhD programs allow you to leave with a Master's if you choose not to continue after two years or so. If you're open to the idea of a PhD but aren't 100% sure, I think going to a PhD program that offers this option would be the way to go. That way, if you decide after a couple years that you really like research and want to continue your PhD route, you can just continue what you're doing. If you decide to leave with a Master's, you could do that too. This would also allow you to be guaranteed full tuition + stipend instead of having to pay tens of thousands of $$$ for a Master's. Obviously, I wouldn't suggest that anyone should go into a PhD program solely with the idea of leaving with a Master's. However, if you think that a PhD might be for you, there's nothing wrong with trying it out and seeing how you like it.

I got a new 2021 Macbook Pro (14" model, M1 Pro CPU) last month to replace my old Windows laptop. Since the Apple silicon chips have been out for a little over a year now, it looks like a fair amount of compatibility issues with various software have been resolved at this point. However, I have seen some issues pop up here and there so far that require a little bit of a workaround (for example, TensorFlow is not currently supported on M1 macs, but it looks like Apple has their own solution to this). As time goes by, I think any lingering problems should be resolved fairly quickly since all of the new Macs are being built with Apple's CPUs. I really like my new computer and don't regret my choice at all. If you like MacOS and the cost isn't too much of an issue, I would say go for it. Tidyverse and other common R packages have worked just fine for me.

For everyone who has received all (or most of) their admissions decisions already, feel free to add your profile to the 2021 Applicant Profiles and Admission Results page! I'm sure future applicants would appreciate it.

I agree with the above statements. Perhaps it goes without saying, but you may need to revisit the list of schools you're applying to and add in some lower ranked schools and drop some higher ranked schools in order to increase your chances of getting a range of options next year. I wouldn't suggest just reapplying to all the same schools unless if you have a much stronger profile by then.

Undergrad Institution: Large private school (top 100 US News) Major(s): Statistics Minor(s): Mathematics GPA: 4.0 Type of Student: Domestic white male GRE General Test: Q: 162 (78%) V: 158 (79%) W: 5.5 (98%) Programs Applying: Statistics PhD Research Experience: About 10 months at my current school (undergrad). Bayesian research in environmental statistics, submitting manuscript within the next month or so. Awards/Honors/Recognitions: Just the usual dean's list, full-tuition academic scholarship, etc. Pertinent Activities or Jobs: Paid research assistant, no TA experience Letters of Recommendation: 1 very strong from research advisor (assistant prof). He told me he would love to have me as a graduate student. 1 probably very strong from an associate professor I took two stat classes with. She told me I was her "ideal student". 1 fairly strong from an associate professor I had for Bayesian stats. We talked a decent amount in office hours, and he was probably impressed that I had already learned all the material on my own. Math/Statistics Grades: Calc 1-3 (A), Theory of Analysis 1 (in progress), Fundamentals of Mathematics (proofs, A), Probability and Inference 1 & 2 (A), ANOVA (A), Intro & Applied R programming (A), Intro to SAS programming (A), Intro to Unix/Shell programming (A), Nonparametric Stats (A), Data Science Methods (A), Regression (A), Computational Linear Algebra (A), Elementary Linear Algebra (A), Bayesian Stats (A), Analysis of Correlated Data (in progress) Applying to Where: (All Statistics PhD) Colorado State University - Admitted on 1/12. Offered GTA with funding of $18,450/9months, health insurance included. Baylor University - Admitted on 1/28. Offered GTA with funding of $20,700/9months with additional $7k fellowship for the first year, 80% health insurance subsidy. University of Missouri - Columbia (Mizzou) - Admitted on 2/15. Offered GTA with funding of $18,026/9months, health insurance included. The Ohio State University - Admitted on 3/2. Offered GTA of $21,280/9months, 85% health insurance subsidy. University of Illinois at Urbana-Champaign (UIUC) - Rejected on 2/24. Rice - pending as of 3/11. Texas A&M - pending as of 3/11. University of South Carolina - pending as of 3/11. Reflection and Advice: I never took any graduate level courses and I'm graduating in just 3 years. I know that if I stayed for another year and took more math classes that I would have probably been competitive for higher ranked programs, but I'm very happy with the acceptances I have gotten, and ranking isn't that important to me since I am almost positive I want to end up in industry anyway. What started out as one of my last choices (primarily due to ranking) actually ended up being my top choice (and the offer I decided to accept). I also think that not having finished real analysis yet may have had a negative impact on my application, but it ended up being okay. My GRE Q score is also quite low, and I did submit it everywhere, even where it was stated as "optional, but recommended" due to covid. For future applicants, I would suggest talking to your professors about graduate school and which programs would be good for you (and where you would be competitive). My LoR writers all gave me great advice and expressed confidence in my success even though I was very unsure of whether I would get accepted anywhere or not. In retrospect, I probably would have applied to some Biostat programs just to have a wider range of options, but I also am completely happy with my acceptances. I'm just really passionate about learning and doing research, so I am excited about the opportunity to start grad school this year.

I found the applicant profiles and admission results over previous years to be helpful while selecting the schools I chose to apply to, and now seems like a good time to start the thread for this year. Copy the template below and fill in as much info as you would like. Keep in mind that you can't edit your post for very long after posting, so it may be good to wait until you have most of your results before posting. Here are some links to threads from previous years: 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020. Below is the template: Undergrad Institution: (School or type of school (such as Big state/Lib Arts/Ivy/Technical/Foreign (Country?)) Major(s): Minor(s): GPA: Type of Student: (Domestic/International (Country?), Male/Female?, Minority?) GRE General Test: Q: xxx (xx%) V: xxx (xx%) W: x.x (xx%) GRE Subject Test in Mathematics: M: xxx (xx%) TOEFL Score: (xx = Rxx/Lxx/Sxx/Wxx) (if applicable) Grad Institution: (school or type of school?) (if applicable) Concentration: GPA: Programs Applying: (Statistics/Operation Research/Biostatistics/Financial Math/etc.) Research Experience: (At your school or elsewhere? What field? How much time? Any publications or conference talks etc...) Awards/Honors/Recognitions: (Within your school or outside?) Pertinent Activities or Jobs: (Such as tutor, TA, etc...) Letters of Recommendation: (what kinds of professors? "well-known" in field? etc.) Math/Statistics Grades: (calculus sequence, mathematical statistics, probability, real analysis etc.) Any Miscellaneous Points that Might Help: (Such as connections, grad classes, etc...) Applying to Where: (Color use here is welcome) School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined

Adding to the posts above, my GPA is high, but my GRE Q is fairly low, and I also have a pretty limited math background (currently taking real analysis). However, I believe my LoR's were quite strong, even though none of my LoR writers are very well-known. On paper, your profile is far superior to mine, and so far I've been accepted to a couple top 30 schools (CSU and OSU, based on US News rankings). I believe you will certainly be a competitive applicant for top 20 programs.

I made my decision already, so I'll give some input on what factors were the most important for me. 1) Work-life balance. Obviously, it's important to enjoy life for the next 4-5 years, which may be difficult to do if you have to spend excessive amounts of time on school. All PhD programs will take a significant amount of time and effort, but there are definitely some that will take more time and effort than others, depending on who you are. Since I am married and planning on starting a family during my PhD program, I didn't necessarily want to choose a program that I thought would consume my life. However, since I tend to be kind of a workaholic, I probably would have chosen a more intense and time-consuming program if I were single. To judge work-life balance, I looked at things like coursework requirements, average time spent on GTA type work, the qualifying exam process, etc. You can get a good idea about a lot of this stuff from most departments' websites, but you may also need to talk to current grad students to get a better idea of what it's like. 2) Program fit. Make sure you do some research into department culture. How do the professors and students interact with each other? Is the department one you would like to be a part of? A department that has a great culture where you fit in well will certainly make your life better than a department that isn't a good fit for you, even if there is a disparity in program ranking. I made sure that every school I applied to had plenty of potential advisors with whom I would like to work if I ended up going there, so that is something to consider in choosing a school as well. If you go somewhere with only 1-2 potential advisors working on some hot topic you're interested in, you may have to compete with a lot of other students to be able to work with that advisor. As a part of program fit, program size may be an important factor to consider. I am currently at a university with a large number of statistics undergraduate students (we have over 500 undergrad statistics majors right now). Thankfully, I have had a lot of great interaction with professors even though we have a large department; however, I have heard that this is not always the case at other universities with large departments, so it's one thing I've kept in mind. Large departments can be great for their resources and research opportunities, but it may also be easy to get lost, and you may have to compete with a lot of others to get time with professors. There are plenty of other factors to consider like location, funding, job placements, etc. All the programs I applied to are in places that I would enjoy living in for different reasons, so this wasn't a major concern for me. The monthly stipend amount (and summer funding availability) is also pretty important, but all the offers I received would have been plenty to live off of in each area, so funding wasn't really a huge deal for me either. Job placements are important, but since I'm 90-95% sure I want to go into industry, I don't think it really matters where I go for my PhD since everywhere seems to do well in terms of industry placements. Good luck on your decision!

How common is it for people to spend several years in industry after completion of a Statistics PhD before entering academia? I've heard plenty of stories of people who start out in academia after their PhD and then move into industry after a few years, but I haven't personally heard many stories of people going the other way. It seems like the vast majority of TT professors start either straight out of their PhD or after PhD + postdoc. This is all in my limited experience, so I'd be happy to hear what you all have to say about it. I ask this because although I haven't heard back from all the schools I applied to yet, I decided to accept an offer at a lower ranking school since it seems like it's the best overall fit of what I want out of a program. I'm about 90% sure I'll end up in industry, but I've always thought it would be interesting to potentially go into academia after working in industry for a number of years. I understand that it typically requires more effort and dedication to enter academia from a lower ranking program, but how difficult is it for those in industry who haven't necessarily been actively publishing in top journals? Any input would be greatly appreciated. Thanks!