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Does Stanford really have an average of 89%? For reference, I looked at https://statistics.stanford.edu/academics/phd-admissions-faq @miserablefunction It seems odd to me that the score bar should be that high for international students at top programs. It strikes me as very weird that a top program like Stanford requires the test, but another top program, Berkeley, does not require the score. If a really high score was an important signal for success in statistics graduate studies, shouldn't all of the top programs require the score (like they do in Math PhD programs) ? The fact that many programs (such as Berkeley, CMU, Duke, Michigan, Wisconsin, UNC etc) do not require let alone recommend the test leads me to believe that the bar is lower. But again, this is just my (very) naive impression. @statfan Your description makes a lot of sense to me. It is kind of unfortunate that programs aren't very transparent about expectations, unlike the math world.

Are there any guidelines for what constitutes a "good score" for Stat PhD programs that recommend the GRE Math subject test? Top Math PhD programs set 80th percentile as a bar, so presumably Stat PhD programs consider lower scores to be good. Stanford notoriously requires the test (and they report an average of 82 percentile), but presumably programs that only recommend the test have lower ranges? Previous threads that have discussed this topic haven't really come to any conclusions, and I wonder if anything has changed since then. Is 70-75 percentile considered "good"? 65-70? At what score should an applicant forgo sending to a program that recommends it?

Thanks for your answers to my questions. Wow, I wasn't aware the academic statistics job market was good to the extent that students could afford to forgo a TT job and still find success later on the market. Maybe those students who are able to earn such a job straight out of a PhD would be able to do so whenever they please, but their confidence certainly indicates that the job market is healthy. In any case, it seems that high impact work is really the key for success, rather than pedigree, which is reassuring. It is also interesting to hear that the theory/applied distinction is not really relevant to hiring decisions. It seems to me that theoretical work is just done on a much longer timescale than applied work. For example, applied ML results are iterated at breathtaking speeds due to the conference format, whereas the reviewing processes in journals is longer. How exactly does a department choose to hire an ML theorist over a ML practitioner specializing in a certain application, or vice versa? Additionally, is there a real pressure to secure grants in the statistics world? Clearly grants are the lifeblood of the experimental sciences and a very large part of a prof's time/efforts is to write and submit grant proposals, dragging the prof away from actually doing science. I have an impression that such pressure doesn't really exist in mathematics. Does the same hold true in statistics? Thanks again for answering my questions. The "business model of academia" is certainly a subject in its own right and it's probably too early for me to concern myself about such things, but it's kind of cool to think about how all of the cogs fit together.

I will be applying to Statistics PhD programs this fall. I had a few questions with respect to forming a prospective school list, which I will include at the end. Below is some information to give some context while still maintaining anonymity (I hope). Undergrad Institution: Top 10 US Private University Major(s): Mathematics and Statistics GPA: ~3.75 cumulative, ~3.88 Math/Stat Type of Student: Domestic Male GRE General Test: 166 Q, 164 V (W pending) GRE Subject Test in Mathematics: 9/15 score pending (I found it tricky lol) Programs Applying: Statistics PhD Research Experience: ~2 years. Preparing to submit a paper this fall. Field is one of {computational biology, chemistry, physics, economics}, i.e. applied area with a tradition of quantitative/computational methods (intentionally vague to maintain anonymity) Awards/Honors/Recognitions: None really Pertinent Activities or Jobs: Grader for various math courses. Software engineering internships for two summers. Letters of Recommendation: 1 "very, very strong" letter from research PI (words from the horse's mouth). Planning to ask two other profs from math/stat courses, which should be solid. Math/Statistics Grades: Multivariable Calculus (A) Intro to Proofs (B+) Real Analysis I, II, III (all of Rudin; A, A, A-) Abstract Linear Algebra (A) Abstract Algebra I, II, III (Groups, Rings, Galois Theory from Dummit and Foote; A, A, A-) Complex Analysis (A) Mathematical Statistics I (A) Intro to Probability Models (essentially stochastic processes; A) Measure Theory (A) Point-Set Topology (B+) Optimization (A) Modern Inference (A) Functional Analysis (A) Misc: i) My research interests skew towards theory (maybe Probability Theory, ML Theory, Bayesian Nonparametrics, or Monte Carlo methods, but I'm pretty open). I like doing mathematics and proving theorems. However, the phenomenon I'm interested in are not in the realm of classical mathematics; I'm interested in the things that statisticians study. ii) The ultimate goal is a career in academia doing research. I understand it is quite difficult to secure a tenure-track position, and theory work seems to be only done in universities. However, I am trying to keep my expectations realistic, and although I wasn't totally thrilled about my software industry internships, it was honest work with its own interesting, technical challenges. A PhD will also lead to more of the interesting work done industry, so this is an upside if I have to leave academia. Nevertheless, I am currently aiming at an academic career. Schools: Applying to most of the "Top 15" (by US News), although I've added a few "safety" schools (is there even such a thing at the grad level?). For sake of clarity, list is below. Stanford, Berkeley, Chicago, CMU, Harvard, Washington, Michigan, Duke, UPenn, Columbia, Yale, UNC, Wisconsin, Cornell, UIUC, Northwestern, Rice. Questions: 1) What are a few safety schools to which I have a pretty solid chance of being admitted? 2) Am I a competitive applicant for the schools listed above? 3) I am aware that the academic job market for Mathematics is probably the worst job market in existence. It is my impression that the situation is better in Statistics, but still not great. It seems like at least one postdoc is needed before becoming a tenure-track assistant prof. Is this impression accurate? Do your prospects change with research area? (i.e. theory positions worse than more applied ones?) Thanks to everyone for taking a look. This forum has been really informative!