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cyberwulf last won the day on September 7

cyberwulf had the most liked content!

About cyberwulf

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    Biostatistics (faculty)

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  1. I don't know of any programs that uses an auto-rejection rule based on a hard GRE cutoff. So, regardless of the range in which they occur (within reason; obviously improving from a 145 to a 147 isn't going to make you more competitive), incremental improvements in the GRE score are likely to have similarly incremental effects on your chances of admission.
  2. Don't sweat it. At worst, you'll make someone reviewing your app chuckle.
  3. Big difference between MS and PhD programs. That score is totally fine for all the former, but might be on the low end for the latter.
  4. Possibly, but such faculty are a dying breed. The trend in biostatistics is towards being more deeply embedded within the biomedical domains they specialize in.
  5. It doesn't hurt to put your GPAs, if they're good. Also, I wouldn't include any kind of summary/objective text on a CV. Regardless, there likely won't be anything on your CV that isn't on your application in some other form (except maybe publications).
  6. I see your point, but being in a department is about more than your dissertation work. Faculty and most students in biostat departments are excited about working on biomedical problems, and so while someone without much interest in biology might be able to get through the Ph.D., they would likely feel quite isolated from their peers and mentors that they don't share a passion with.
  7. In normal times, having your own funding might be a "tie-breaker" if you're on the admissions borderline for a program, but wouldn't give you a big leg up. With COVID, I guess I could see a slight benefit in a situation where, say, you're ranked 20th and due to COVID they're only admitting 15 but they decide to extend you an offer since in normal times they would have admitted 25 and you're essentially "free" to them because you're coming with your own funding. But obviously that's a pretty special set of circumstances.
  8. Yes. You'll probably find as you get engaged in research you'll learn (and hopefully become interested in) some of the underlying biology related to the problem(s) you're studying. A lack of interest in biological applications would make someone a bad fit for a biostat program, but a lack of experience and knowledge isn't an issue.
  9. It's going to be really program dependent. I would guess that cohort sizes in statistics departments (main grad student support source: TAships) will decrease more than cohort sizes in biostat departments (main grad student support source: NIH grants) because universities are feeling the financial pinch but federal research funding hasn't yet been drastically affected by the pandemic. Another thing working in favor of biostat departments is that COVID has driven a lot of interest and investment in public health, so there is even more demand for health statistics expertise. And of course,
  10. Your friend may be slightly more competitive than usual this year because, as others have noted, their GRE is a major weakness and many programs are not requiring it. That said, they should probably be looking outside the top 10 Biostat programs for PhD admission. However, they would likely be admitted to many Masters programs, even some in ranked in the top 5.
  11. The letter from your Masters advisor will carry by far the most weight; the others are (relatively) less important. Seems like letter writer #2 would be your best option.
  12. There's a lot more stuff that I would call "research lite", for example REUs, summer internships with professors, senior research projects, etc. However, since many applicants now have this, and the fact that it's almost impossible to gauge how much a student has really contributed to any research outputs they list, it doesn't really move the needle that much. Also, these opportunities are much more readily available to students at larger and more elite institutions, and it doesn't make sense to penalize those at smaller colleges who might have excelled if given these "research" opportunities
  13. I'm sorry, I just can't let this stand unchallenged. It is complete nonsense to say that GLMs have had little impact on data science. Talk to any practicing data scientist and they'll tell you that a lot of the models actually being used in practice are relatively simple regression models. And survey sampling? That's a special case of weighting, which is heavily used in machine learning in the case of rare events (and also to increase algorithmic fairness). If all you're interested in doing is creating algorithms that do something faster or more accurately, sure, maybe you don't need a t
  14. I would definitely prioritize taking analysis and probability (in that order of priority) over differential equations.
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