Counterfactual

1) I don't know how switching majors at your school works but I believe in many schools in asia, GPA matters a lot when it comes to changing majors. Though, even without a math/stat major, you're math background is sufficient for getting you into a masters/phd program in stats or biostats. Though I do understand that some graduate schools do encourage a degree in related fields rather than a biology major + math/stat coursework. 2) I don't think one needs to discredit a major to justify a switch of majors. Instead, say more about why you chose ds and how it interests you. Maybe it's in the analysis of biological data that you realize you'd enjoy developing algorithms to help (etc quicker computation, higher accuracy) estimation. Or maybe you just know that you enjoy coding, math, and stats, and also did well in related classes.

You're profile is very competitive for top 10 biostats programs! I'd apply to every program that I'd want to go to if I were you. Your math background is sufficient for both statistics and biostatistics. Most programs only requires calc 1-3, analysis, linear algebra, a course in math stats/probability anyway, and you went beyond that and did well in many of the upper div classes! In terms of the P's, don't let them bother you since they can be explained away in the SOP. I believe it would suffice to let the admission committee know in your statement that they were due to the school policy and the pandemic. Regarding switching fields, it is not uncommon for econ majors to switch to biostats or stats! I envy that you already have experience with dealing with projects related to public health policies prior to applying. I applied to biostats program with a double major in finance and statistics with very little project experience related to public health or medicine. The furthest I went was to take courses related to causal inference and mention my literature review project that is related to estimation methodology commonly used in the field of public health. So you're way ahead of me in terms of intention to switch fields! No worries! One of the takeaways I had this application cycle is to apply to a larger number of programs. If you have money to spare and the LoR writers don't mind, I think it's worth the incremental effort!

Undergrad Institution: University locally known for business & social science in Taiwan (ranked around 200 globally in business in USNR) Major(s): Double Major in Finance and Statistics Minor(s): N/A GPA: 4.29/4.3 (4.0/4.0 after WES evaluation) Relevant courses: Calculus (A+/A+), Linear Algebra I/II (A+/A+), Mathematical Statistics I/II (A+/A+), Regression Analysis I (A+), Multivariate Analysis (A+), Programming 101 (A+, in Python), Programming and Statistical Software (A+, in R) Exchange program institute: UC Berkeley (one-year international exchange program) GPA: 3.95/4.0 Relevant courses: Analysis I/II (A/A-), Statistical Prediction and ML (A), Causal Inference (A), Grad Experimental Design (A), Grad Intro to Stat Computing (A) Type of Student: International (Asian) GRE General Test: Q: 166 (84%) V: 157 (74%) W: 4.0 (54%) GRE Subject Test in Mathematics: N/A TOEFL Score: R30/L28/S25/W28 Programs Applying: A mix of Statistics/Biostatistics Masters and PhD programs Research Interests: Causal Inference, Network Analysis Research Experience: 1 conference paper in community detection. I worked as a student researcher at a statistical institute in Taiwan, main theme of the research is community detection algorithms. Awards/Honors/Recognitions: Presidential Award x6, Study abroad scholarship, Research Scholarship Pertinent Activities or Jobs: N/A, nothing beyond my undergraduate research Letters of Recommendation: 4 letters from instructors at Berkeley (Likely the typical did-well in class letter), 1 from my undergraduate research supervisor (strong) Any Miscellaneous Points that Might Help: The letter from my undergraduate research supervisor should be very strong, he said he would love to have me as his PhD student, and I think that definitely helped tremendously!! Applying to Where: (Color use here is welcome) Ohio State University Biostatistics PhD - admitted 1/26 with official letter received 3/1 Iowa State University Statistics PhD - admitted 1/31 University of Michigan, Ann Arbor Biostatistics PhD - admitted 2/9 - Attending!! University of Florida Statistics PhD - admitted 3/7 UC Davis Statistics PhD - interviewed on 2/15, admitted 2/25 University of Minnesota, Twin City - MS Biostatistics - admitted w/o funding 2/4 (rejected from PhD) Harvard University SM 80 Biostatistics - admitted w/o funding 2/10 UW - Seattle Biostatistics MS Capstone - admitted w/o funding 2/14 UC Berkeley Statistics MA - rejected 3/11 UW Seattle Statistics MS - rejected 3/27 UT Austin Statistics PhD - rejected 3/27 University of Michigan, Ann Arbor Statistics PhD - rejected 3/28 UW Madison Statistics PhD - rejected 3/29 UC Davis Biostatistics PhD - rejected 4/6 Penn State University Statistics PhD - rejected 4/11 Purdue University Statistics PhD - rejected 4/11 University of Minnesota, Twin City Statistics PhD - rejected 4/14 Ohio State University Statistics PhD - rejected 4/18 Texas A&M Statistics PhD - rejected 4/29 NC State Statistics PhD - rejected 4/20 UCLA Statistics PhD - rejected 5/19 Thoughts and Thanks: I feel super blessed this applications cycle and am so very grateful for the admission results. I know for a fact that none of the above are safety schools, and though out of the 21 schools I applied to, most resulted in a straight rejection (didn't even make it to the edge of the waitlist). At the end of the day, I got into many schools I never thought I would've gotten into. I didn't have a strong math background compared to most of the international applicants, nor do I have fabulous GRE scores (my quant section was a mess). I didn't have a 3-stack strong letter that are all from reputed professors (my letters were a mix of did well in class letters from Berkeley and one strong letter from the professor whom I did research with in Taiwan). Other than a great undergrad GPA, I think the SOP carried more weight than I thought it would. And submitting a large number of applications to schools with matching interests was also definitely helpful, though hindsight 20/20. I've been following the forum ever since I was a freshmen in college, and I'm glad I finally made it to a PhD program!! Special thanks to @bayessays@Stat Assistant Professor @cyberwulf @trynagetby and many others! Your career and application advice helped me tremendously in terms of career planning and in the application process!! I know there's a long way to go, but I am glad I can be making contributions to the scientific community through developing better estimation methods and help create better decision criteria via math and data! Can't wait to start the Fall semester!

Probably going to decline the MS offer. They said in the letter they are unable to offer graduate assistantship at this point. The letter was super heartwarming though. I'm also waiting on NC State and UMich Biostats! Hope we both get in!! Also, congrats on getting into UMN biostats PhD, I've heard nothing but great things about the program!

Just heard back from Minnesota Biostats. I applied to PhD and was offered admission to masters.

Hi, also a fellow applicant here! I believe each application cycle is independent. I don't think it make sense to have such a system, though I do understand that some schools would ask you whether it's you're first time applying, but I honestly don't think its designed to do the screening. So here's an example, not in stats though. I saw someone rejected from every neuroscience phd program the first time they applied to. After an additional year of research, with stronger letters, they got into the very schools that previously rejected them. Though the above story is only sample size 1 in a different field, I do believe many similar cases exist. Though, probably also consider applying to a wide range of schools.

Most of them are in their third or fourth year when we met. Though in terms of career path, to the best of my knowledge, at least two were determined to go into industry before matriculation. (But I could imagine that they probably did not explicitly show that on their SOP/PS) So yeah, I'd say there's still time to figure that out along the way during PhD if one's not sure. But if you already know that you wanted to go into industry, there are people in top programs that have the same goals too. Even so, from my humble experience, it never hurts to have an open mind career-wise.

The observation is based on small sample size, but I do know a Stanford and several Berkeley PhD students who decided or are thinking about going into industry upon graduation. So I guess it's hard to say.

That being said, I think you could still probably give decent masters programs requiring GRE a shot depending on your background (coursework, grades, other experiences). I knew a senior who applied to statistics masters programs in 2019 (matriculated in 2020) with a GRE < 310 (presumably not so high quant scores). He got into UC Davis MS stats (w/ fellowship), U Florida MS in biostats, and UCR MS stats. Though during his undergrad, he also did a double major in statistics and performed quite well. In addition, he had some statistics related REU at a research institute. So I guess in the case above coursework, grades, and other relevant experience probably mattered way more than GRE. Besides, I also saw someone reported to be admitted to JHU with a quant score of 159 on another stats forum. Even though the admission was said to take place several years ago, and their background and programs they applied to are not clear (probably stats related given that the comment was on a stats forum). So if I were you, suppose that I have a decent background and some money to spare, I'd probably give some schools that require GRE a try, too. Hope this helps!

Your Q probably needs a boost if you decided to submit GRE. For international students, I think getting a 165+, or more conservatively speaking, 168+ in the quantitative section would be more optimal. Even so, many programs waived or made the GRE requirements optional this year, so you might also want to consider those programs.

@bayessays Thanks so much! That's super helpful!

I was wondering if it is looked negatively upon if I have no recommender from my home university (I studied abroad for an academic year). I’m an international undergraduate student in my senior year planning on applying for Stats/Biostats master/phd programs. Here’s a distribution of my current letters: 1 letter from my PI in the research institute where I did my REU (should be strong) 2 letters from course instructors in the university where I studied abroad (big names but probably did-well-in-class letters, should’ve attended more office hours then:( ) I don’t have any letters from my home university, since most of the professors I knew well retired and are difficult to contact. My supervisor for the REU said it would be weird to not have any letter from my home university, so urged me to ask for a letter there. I’m not sure if I should stick to my current plan, or replace a letter as recommended by my supervisor. Any advice from this forum would be extremely appreciated! Thanks!

Hi, I'm also applying for Fall 2023 stats/biostats phd programs. I think your mathematical background definitely suffices. Most programs only require the calculus sequence + one course in linear algebra + one programming course + real analysis I + probability/math stats. Good scores in other upper div/ grad math courses is a plus but not a must, especially for biostats phd programs. So if I were you, I'd just apply directly without taking any additional math courses. And probably ask a professor whom I have taken upper div/graduate math classes with to write about math ability if necessary. (Though if you already have better letters from professors whom you know well, I'd just stick to those) Good luck :)!

Hi! I am an undergrad currently doing research part-time. I’d like to share my experience regarding your second question. I applied for research related positions at my school and other research institutes via email, which includes, 1. A single-page CV, 2. A copy of my official transcript, and 3. A statement of purpose, stating my background, why I would like to conduct research, and my future plan. I recommend submitting to professors/researchers whose research interests intrigue you. Many of them have their interest posted on either the faculty page or their personal websites. It might also be a good idea to skim through the titles of their publications to know what they are doing. After that, if lucky enough, some may respond and ask you over for an interview. From what I have experienced, the interview would be less technical and something like: What kind of research topic would you like to do? How long would you be able to stay in the research group? What would you like to do in the future? etc…. They normally don’t expect you to know too much of what they are doing. About your first question. I believe if you have done well in the upper division math courses, the P/F would be fine. Though this should probably be answered by someone more experienced. Good luck!

@bayessays Thanks for your response! I've always gained much from your advice! I will definitely give it a shot :D! Thanks again.

I am currently a junior student working part-time in a research group focusing on network science, more specifically speaking, community detection in networks. Community detection is interesting, however, I have always wanted to know more about research in biostatistics related fields, such as survival analysis or causal inference, which are two things my PI does not do. (In his words, he does anything but those two). A professor I know happen to be conducting a research project on ‘' Mean residual life models for survival and longitudinal data’’, which I am very curious about and would be willing to try. So I was wondering whether it would be appropriate, given consent from both PIs, to work in both groups simultaneously. Opinions on different forums do not lead to a consensus. Although most answers indicate that working on both is morally acceptable as long as both PIs approve of it and are not competitors in the field. Some say they do not recommend doing so given the time constraint and that such behavior seldom leads to meaningful research results; others say it’s worth trying as long as you put enough effort in both places. I am now torn between the two ends. So I would really love to seek opinion from one of my favorite forums. Thanks a lot! P.S. The research in community detection is funded, while the project on mean residual models will likely be a volunteer position.