Geococcyx

I agree with bayessays, but I did want to add in one edge case -- I really wanted to go to CMU in part because of their strong collection of people working on applications in sports (more the students than the professors I think, but they host a summer research program for sports statistics too). Of course, that's a niche interest, so YMMV.

(They only do grad program rankings every several years (4, maybe?), so 2018 should be the most recent)

I don't have any good method of finding programs without GRE requirements, but: Minnesota biostatistics appears to have dropped its GRE score requirement (as part of the whole school of public health dropping the GRE). That was announced in June/July during the pandemic, but to my knowledge they were already looking at that before the pandemic and they'll continue not using GRE scores thereafter. Maybe that's the program dmacfour found that dropped their requirement, or are there others? In any case, we could probably crowdsource a list of programs that don't require GRE going forward as programs decide whether or not to reinstate GRE requirements (per bayessays' point).

They mean accepted 12, since they only had 3 matriculate (it's a small department, at least at the PhD level). You can find the data here: https://gradschool.duke.edu/about/statistics/all-departments-phd-and-masters-admissions-and-enrollment-statistics I have less than zero knowledge of how admissions work, but I'll say I'm skeptical that Duke would interview over a third of their applicants.

If you haven't, you may consider looking through the comments on past years' results in the results search -- I know I tried to document first year stipend offers there, and I think some other people did as well. MLE's Duke first year offer seems about the same as mine was, so I guess that's stayed constant. Most of my other first year stipend offers were more 20 K-ish (over 9 months) -- about 20 K at Illinois but with an extra 4-5 K the first year, 20-21 K at Ohio State with an extra 3 K the first year I think, 21 K at UCLA statistics but with 7 K in housing stipend for the first year, and then around 19K at NC State (which was an admit off the waitlist). Granted, any of those places except UCLA probably aren't anywhere near the housing expenses you'd have in California (which seems to be where all the schools you applied to are, per your signature).

To your last question, it's certainly possible for programs that typically interview to accept candidates without interviews, since they may just choose to not do interviews for some or all students that year -- for instance, I think Duke used to do interviews before my application year, but I know they didn't do interviews my year (at least for domestic students) and so far as I know they didn't go back to interviewing people. I imagine the answer to your middle question depends on the program, although I can't pretend to be an expert. Maybe someone else can help you with specific schools in mind. For your initial question: I had one interview, and for that I just got an email invitation to a "recruitment day" for top candidates, and heard something akin to an accept/waitlist/reject a month or two later. I think I applied to one or two programs that also did interviews that year, and was aware interviews had happened at each place well before I got a decision notification (which were both rejections, as expected). That said, this is all pre-COVID, so for all I know admissions committees still aren't sure how they're going to do things this year.

I really doubt departments would throw your application out even if they realized you sent them a generic personal statement. Most folks' personal statements are mostly generic with some slight additional commentary on their interest in that particular school; a fully generic statement isn't exactly amazing, but it's really not that out of the ordinary. Hell, I think I got into some schools that I sent a fully generic personal statement to. In any case, are the admissions committees in these departments really going to run over your personal statements line-by-line to compare them? I sincerely doubt that's the best use of their time. So, I really wouldn't worry about it; you're probably good to go.

The above panel is recorded and will be posted on that website if you missed it but wanted to watch; meanwhile, if there's any further interest in this kind of thing, Washington statistics announced something similar as well (albeit on November 9th at 7 Eastern/4 Pacific, so with more lead time): https://stat.uw.edu/news-resources/articles/statistics-will-be-hosting-virtual-graduate-student-panel, or registration here: https://washington.zoom.us/webinar/register/WN_wthwEZe7RIiBh7eCED8u9w

You can definitely find the sorts of statistics you like in biostat departments. Just to drive the point home, it looks like you did some research on Bayesian classification -- a reasonable example of how that may work in biostat may be Briana Stephenson, who worked on Bayesian clustering methods at UNC biostat (and now is a prof at Harvard biostat). Out of your other possible interests from your profile: plenty of biostat folks do causal inference (e.g. Hudgens, also at UNC), and for your more machine-learning sorts of approaches, one example may be the sort of precision medicine work that Kosorok at UNC biostat and Laber at NC State (which is combined stat/biostat) have done. These examples are obviously north carolina-slanted, but this isn't a unique situation (e.g. Witten does biostat ML at UW). To StatsGOd's point, most of these applications don't require that you be an expert in biology (and correspondingly, there are plenty of geostatisticians who aren't earth scientists and whatnot). I think bayessays has some nuanced opinions about whether biostat departments are the right choice for you in terms of being miserable or not; I'll let them weigh in if they desire and eschew addressing it myself.

Saw this virtual panel apparently from UC-Berkeley and Michigan Stat PhD students to answer questions about Stat PhD admissions and whatnot, and figured people who come here might also be interested in it. I claim no knowledge of these folks, but Rob Santos tweeted this out, so I'll take the liberty of assuming he hasn't gotten hacked or whatnot (and I haven't seen anyone else post it; if they have, we can delete this). Anyways, the panel is at 7 PM Eastern time on Oct 13 (today/tomorrow, depending on how you delineate that). Here's the link: https://www.statsphd.com/ to their site, or the Zoom registration directly to save you a click: https://umich.zoom.us/webinar/register/WN_R9Bjv3IdRoqmrdygdnkNPA

I have a sneaking suspicion someone (likely Stat Asst. Prof) already answered this, but just in case I've misremembered: is there any school/course that comes to mind as what you want to see in an updated statistical inference course? The closest thing I've seen is Stanford's 300c (here: https://statweb.stanford.edu/~candes/teaching/stats300c/index.html). (I'm not really experienced enough to have overarching opinions on Stat PhD curricula, but I'll second all the suggestions for more computation, and clarify that in my experience, some emphasis/additional emphasis on algorithm design, numerical linear algebra, matrix decompositions, and maybe factor analysis/matrix-based models would be nice as part of the core curriculum.)

The previous posters are all the answer you need, but just to add: the letters will probably be identical/mostly identical for all of your schools, so I wouldn't be exceptionally worried about the toll on your letter writers provided you give them appropriate lead time and info to actual draft their "master letter". That's especially true if you're applying to some schools via SOPHAS, since if I recall correctly they would only need to upload their recommendation once regardless of the number of schools (if the letters aren't super targeted towards a particular school, of course). (As a disclaimer, I feel I should note this is my experience with recommendation letters -- maybe letters are more specifically customized for each school for super elite candidates, or perhaps if you go to a smaller undergraduate-oriented institution wherein having some previous connections between the college and graduate program is helpful; I am neither elite nor from an undergraduate-oriented institution, so I cannot say) I was also worried about the work it would take to send out 20+ applications (I got to 14 I think), so I held off applying to some places I was interested in with later applications deadlines so that I could still apply to them on-time if I wanted, but I could avoid working on them if I'd already gotten into somewhere I preferred better. For instance, it's entirely possible to apply to (say) Ohio State or Illinois and get accepted before you even reach the application deadline for (say) TAMU and UCONN, at least recalling the deadlines in my application year. Granted this is mildly in conflict with what I'd recommend in terms of giving yourself the option to learn everything you can about each school before you decide to turn them down, but if you need to save time then it can work.

I won't comment on your application's strength, but some other folks (and their respective schools) where you might find spatiotemporal modeling: Daniel Simpson and Vianey Leos-Barajas at Toronto (I know someone already recommended Dr. Simpson, just included for completeness' sake), Rick Schoenberg and Karen McKinnon in the UCLA statistics department (not sure how easy it is to do cross-advising between biostat and stat there), Brian Reich at NC State, and Matthias Katzfuss at Texas A&M. Additionally, for wearable technology, I tend to think of Vadim Zipunnikov at Johns Hopkins and maybe Julian Wolfson at Minnesota, but there are probably many folks I just haven't found. This isn't really my area, so I hope these are good enough folks to look at as a start until you can get into the papers and see who's getting cited, and that I haven't accidentally misrepresented anyone's research interests. Most of them do at least focus on environmental applications, which tends to be a good start.

Stat Assistant Prof answered some of your non-teaching questions here: https://forum.thegradcafe.com/topic/116685-good-productivity-benchmarks-for-a-strong-research-advisor

Some of OP's questions seem like they might be answered with the slides and video recordings from eCOTS's workshop on preparing for teaching-focused faculty positions; here is the website to access that: https://preparingtoteach.org/agenda/ (there was recently a review of this posted via Sara Stoudt and Mine Cetinkaya-Rundel, posted here for a quick summary: hathttp://www.citizen-statistician.org/2020/06/preparing-to-teach-2020-what-did-we-learn/). That said, they seem to largely agree with our experienced posters, so this may not be worth your time.