Taheel

I think you should definitely look into UNC's biostatistics program. UNC's program is known for being theoretically rigorous but it's housed in the Gillings School of Global Public Health which is one of the top public health departments in the country. When I visited last year, they devoted some time to discuss their certificate in global health and it seemed like a really great opportunity particularly if you're interested in working on international projects. If I remember correctly, there's some required courses/seminars as well as an (international?) internship. It seemed like they were trying to get more biostatistics students into the certificate program, so it would probably be pretty easy to get into if you express interest.
If you'd like to reach out to the department to learn more, I believe Annie Green Howard is the person to talk to.

If you don't have professional experience, I wouldn't apply to UoC, from my experience applying, they seem to favor applicants with a professional background over straight from undergrad candidates. Also, I can't remember how much flexibility you'd have with your schedule, but I know the CAPP program has less space for electives than their MPP program because of the computational concentration. 
Looking at Urban Planning programs with a computational aspect might be a good way to go, but I'm guessing you've done some digging there. If you're currently taking math/CS classes to supplement your undergrad courses, I would say try Indiana University's Informatics program, they've done some stuff recently with Urban Informatics. 
Honestly, depending on how "not great" your GPA is and what your exact grades situation is, go for applying to the more reputable programs, because grad school is a different game re: getting in. If you've demonstrated that your grades have gotten better through your undergrad, I think admissions are more interested in what you've done recently over how you started out as a Freshman. 
Also, consider exactly what kind of thing you want to be working on and search for professors working on the same thing, like, get on Google Scholar and search for research that interests you, because those professors will be working in programs that might work for you, even if they aren't what you expect. This is especially true in more academia/research-oriented programs as opposed to professional programs. (I ended up applying to BU's Emerging Media Studies program as well as Georgetown's and UoC's because I'm interested in politics/policy and news media as a tool for policy analysis, which is something profs there are working on.) Plus, demonstrating a specific affinity for the program and work being done through the program is a big step up in writing your statements of purpose and can help with "making up" for less than perfect grades. It shows that you've thought hard about what you want to do and how the program in question fits you and how you fit it. 
Also also, side-note: from what I've found, many of these programs, because they're more professionally oriented, don't supply a lot in scholarship/fellowship/assistantship money (or, well, they do but the starting rate is hella expensive), so taking this time to look into outside scholarships/fellowships would also be a good idea. 
Okay, that's an essay lol, but background on me: I'm an English major with a CS minor and I want to work in policy analysis, journalism, and (news) media criticism, applied to Georgetown's MSDPP, Claremont's MSADS&IR, UoC's CAPP, BU's EMS, and IU's Informatics MS. 

It may be a good idea to spend some of your free time reviewing:
- basics from Calculus I-III (specifically: the derivative rules like product rule and chain rule, integration and rules for integrals like u-substitution and integration by parts; partial derivatives, double/triple integrals and changes of variables/Jacobian matrix, and sequences and series... you don't really need to review things like cross products or torque)
- linear algebra (both basic and proof-intensive)
- basic real analysis (at the level of Abbott's textbook).
It is natural for a lot of people to forget things from these classes if they have not encountered/consistently worked with those tools in awhile, but it is a good idea to refresh your memory before starting. I find the MIT OpernCourseWare (OCW) to have useful resources for reviewing these things. 

I think UNCs honestly not too bad compared to those schools (especially Harvard/JHU/berkeley once cost of living is factored in), out of state is 15k a semester for UNC, far higher at the other places. I receive in state tuition to UMN, so it will be nearly half as much to get my MS, which is a no brainer to me. And that's assuming I don't get a graduate assistantship lol. Plus I'll be closer to my family and my dogs need cold weather! If the costs were closer I'd definitely consider UNC, but regardless I'm sure happy to get an admit!

Another thing worth noting is that if you go to Minnesota you have the potential of getting an RA position that would pay your tuition. A 50% appointment pays your entire tuition and a 33% appointment pays half your tuition. I would reach out to the graduate program coordinator to ask about it.
 
I don't think private schools like Emory are be able to do that (I could be wrong).

This is opposite from the advice I've heard elsewhere. 

Absolutely DO NOT do this. You can ask current students questions if you visit or if the department connects them with you explicitly, but it would be extremely inappropriate for you to seek out students or alumni to talk to on your own. 
 
I would say the things to look into depend a lot on your goals and what you want out of the program.  Your list is a good start.  I'd add that you will want to look at the alumni placements and the curriculum.

Undergrad Institution: Large state school
Type of Student: Domestic white male
Major(s): Chemistry, Math
GPA: 3.71
GRE General Test:
Q: 166 (91%)
V: 165 (96%)
W: 5.0 (>90%)
Grad Institution: Large state school
Concentration: A few post-grad courses in statistical programming/applied statistics
GPA: 4.0
 
Research Experience: 2 years of research in chemistry, 1 publication
Awards/Honors/Recognitions: Graduated with honors, award for undergraduate research
Pertinent Activities or Jobs: (1 yr) Healthcare IT company, (1 yr) Work as an research analyst/data manager for a well-known academic medical center
Letters of Recommendation: 1 from math professor, 2 from research advisors (chemistry professor, biology professor)
Math/Statistics Grades:  A in calculus sequence, A in probability, A- in real analysis I, A in linear algebra, no mathematical statistics, 3.85ish math GPA (all B+ or greater)
Applied to:
Biostatistics
UNC Chapel Hill - PhD / Admitted 
University of Minnesota - PhD / Rejected from PhD, admitted to MS
University of Michigan - PhD / Admitted to fast-track MS/PhD
Brown University – PhD / Rejected
Harvard University – PhD / Rejected
Columbia University – PhD / Rejected
Statistics
UNC Chapel Hill – MS / Never heard back?
University of Wisconsin – PhD (Biostats concentration) / Waitlisted - Admitted
University of Minnesota – MS / Admitted
Duke University – MS / Admitted
Ohio State University – PhD / Waitlisted - Rejected
Penn State University – MS/PhD (joint application) / Rejected
NC State University – PhD / Waitlisted – Rejected
Wake Forest University – MS / Admitted
 
Thoughts:
I came in not really knowing what to expect and not having a clear idea about what schools I should aim for. In hindsight, I shouldn't have applied to Columbia and would have added in UC Berkeley biostatistics (MS) in its place. On the statistics side, I wished I had switched Colorado State in place of Wake Forest. Other than that, I'm pretty happy with the list of schools I applied to and I ended up having some great options to choose from. 

OMG I got a last minute offer! I'm so glad and relieved!

Thanks everyone! As an update, I've decided to attend UT Health and just be really involved in research. I've spoken to a few professors and definitely feel that this is the best choice in the long term.

DEFINITELY, and I don't regret it (I submitted my decision early this morning ha!). I made a list of three objective arguments, plus it just clicked for me . Anyway, honestly I think any of the two schools would have been a great place, no doubt.

Just for all prospective students who may find this thread and read my comment... Rankings are not everything. Your mentor is far more important than your department's ranking and you ultimately determine most of your own success. I'd be remiss not to mention that at large, high tier departments the selection of quality mentors is larger than lesser departments, but these departments can also be a lot more competitive in terms of getting to work with a specific person. Great researchers can come from mediocre programs, and mediocre researchers can come from great programs, and you can find a great mentor/adviser at places other than Harvard, Hopkins, and UW. 
I've been on these forums since 2012. I started grad school in Biostatistics in 2013 at a department that isn't in the top 10 of these rankings.  To inspire a little hope for most  people that do not have the privilege to attend a top-tier program, you can still be successful and pursue a career in academia! To be transparent, my goals have never been to have a career as a full-time methodologist. I'm pursuing an academic career as more of a collaborative biostatistician that works on methodology, but not exclusively. I have over 5-10 publications from grad school, most in clinical journals but a couple in stats journals at the level of Biometrics, JRSS-C, and Statistics in Medicine. I have won student paper/travel awards for multiple conferences, including a first place student-paper award at JSM. I've been an invited speaker at several internationally attended meetings in my area of research, having traveled throughout North America and Europe. I'm finishing my PhD this semester and I received multiple offers at the assistant professor level to various academic institutions. Now keep in mind, I'm not pursuing a career primarily as a theoretical/methodological statistician, but the offers that were made to me were from Biostat departments at several institutions ranked in the top 30 for  research universities and have highly regarded research hospitals affiliated with them. 
Look, what I've trying to say to everyone who is considering/attending a program outside of the top 10, is that you are ultimately responsible for your own success. You can still win awards, publish in good journals, and get good job offers in academia. Do not believe that unless you attend Harvard or Washington, you can't get a job in academia, because you absolutely can. And I am not an anomaly from my institution either, several other students that have graduated from my department are at top 30 research universities, and a few in top 10 biostat departments. So, good luck to everyone and just know that rankings are not everything and do not decide your fate. I encourage prospective grad students to PM me for advice if you want - I'm always happy to help. I received a lot of great advice from here ~5-6 years ago when I was applying to school and deciding where to go, so now having been through it, I'd love to pay it forward. 
 

Wait are we talking about the same "ISU"? Mine is Iowa State, program Bioinformatics and Computational Biology, which is only somewhere near Statistics.
For me, I don't know but I have felt really connected to the place right when I interviewed with the school. In fact, I applied to ISU after I got accepted to NCSU haha, I really don't know why I even submitted! I like how they designed the program, and with the lab rotations and having two advisors things, I feel ISU will be a better fit for me. Working in industry is not my plan; I love teaching and want to keep working in acamedia and become a lecturer in the future.
I'm still considering you know. It's a tough choice. But I feel I won't regret going to ISU.
So here are some things people advised me: NCSU has a higher ranking (#10 in Stat this year) and well-known program of Stats; locates in a warmer and more dynamic place, which will help a lot if you plan to work in industry. ISU also has a good program (#13 in Stat this year) though less famous than NCSU's, and life in Ames is very peaceful. The two places are toss-up actually.
I wish I could know something more to help both of us. But deep down here I hope you can choose the right place for you!

No idea how large the waitlist is but they're hosting a visit for admitted MS students this Friday - some movement might occur after that. I'd expect at least 1 position to free up 

Here's what they say about methodology: 
" Rankings of doctoral programs in the sciences are based solely on the results of surveys sent to academics in biological sciences, chemistry, computer science, earth sciences, mathematics, physics and statistics. The individuals rated the quality of the program at each institution on a scale of 1 (marginal) to 5 (outstanding). Individuals who were unfamiliar with a particular school's programs were asked to select don't know. Questionnaires were sent to the department heads and directors of graduate studies at each program in each discipline." 
There were 109 institutions surveyed for statistics/biostatistics with a response rate of 35% (roughly 76 respondents if the questionnaires were sent to two people from each institution).

To me, the most important takeaways from this are A. the rankings say virtually nothing about reputation/outcomes for industry as academics are not likely aware of this, B. while a difference in average score of 0.1 or 0.2 can significantly affect ranking (e.g. #27 is a 3.6 but #37 is a 3.4) the 0.1/0.2 difference is likely meaningless given that response rate was 35% and we don't know who actually responded, and C. the rankings don't directly account for student outcomes, size of program, research production, or any other objective metric.

That being said, I think it's really impressive that Stanford got a perfect 5.0 and the next highest rated school (UC Berkeley - solo) received a 4.7.

CMU has just closed their waitlist. I have decided to go to UChicago and decline Purdue and UIUC. Best of luck to everyone! It was a stressful period of time, and now I can focus on more constructive activities.

There are a few other current PhD students who frequent this forum. I've visited it on and off over the years, but I have not seen many posts from current PhD students about their experiences. I thought this may be of interest to potential applicants, so I decided to write about what I have learned (I am about to graduate, finishing my final defense and thesis in May). I am happy to report that my PhD experience was largely positive. 
1) A PhD program is fundamentally a research degree, and research is nothing like taking classes. I think some Stat/Biostatistics programs do a great job of involving students in research early on through rotations with different professors or through reading courses to familiarize students with statistical literature. But there are a lot of programs where students do not start research until the end of their second year. And I have seen many PhD students who were very, very bright (acing all their classes, 4.0 GPA, etc.) but who really struggled with transitioning from being a student to becoming a researcher.
I definitely think you should work hard in your classes so you can pass your written qualifying exams and so you can developed a solid foundational understanding, but once you get to the research stage of the program, you really do have to teach yourself a whole new area. Moreover, research is about discovering something new and pushing the boundary of your field. There is just no way of knowing if some "open problems"  can be solved or not! It's not like solving a problem on a homework set where there is generally one correct solution/approach. If you do a theoretical topic for your dissertation, you need to prove new theorems that have never been established before, not just "show” something that already has a known solution. And even if you start working on a problem, you may get stuck for long periods of time (or need to cut your losses and give up), or you may end up somewhere completely different from where you started. Unlike problem sets and exams, there are no concrete solutions. For example, for the first paper that I wrote, I was stuck on a proof for my main theorem for three whole months. Nothing I tried seemed to work! But my PhD advisor pushed me to keep trying, and eventually I found the technique that worked. Phew!
2) A lot of the learning in grad school happens outside the classroom, and you need to ask questions. This comes from talking with your peers, meeting with your advisor, attending departmental seminars, and reading papers. Here is the thing: when most people start research, they do not yet have the skills to really excel at it. A small number of people are able to excel right from the get-go, but for most people, it takes a bit of adjustment, and that's okay! It is important to reach out for help if you need it. If I didn't understand an author's proof or a new concept that I had never encountered before, I would ask my advisor to help me. I didn't have much experience with high-performance computing or running simulations on multiprocessing systems, so I asked my more experienced classmates to help show me how to navigate it.
3) Everybody thinks about quitting at some point. This is perfectly normal. A PhD can be a very demoralizing, frustrating experience. Plus, things can happen in your personal life that can derail you. It's just part of life. When I felt like quitting, I just took some time off... maybe 2-4 days of not doing any work to recuperate and assess why I was putting myself through the PhD. After some time off (not too much time off), I could reason to myself why I wanted to get a PhD, and I got right back to work. So if this happens to you, accept your feelings, take a breather, and then really question your own motivations for pursuing a PhD. If you can answer this question to yourself, "Why do I want a PhD? Am I willing to 'tough' it out when I'm feeling frustrated?", then you will be able to pick up right where you left off.  
4) Just about EVERYBODY gets their papers rejected, even Distinguished Professors and Nobel Prize winners. My PhD advisor has co-authored over 250 papers and is quite smart, and he still has papers rejected. Professors at all levels get their papers rejected, some multiple times before they are finally published. It’s part of the process.
It also happened to me for the first paper I ever submitted. Rejection always stings, but I say if it happens, take a deep breath and cool off a bit. Once you’ve acknowledged the disappointment and cooled off, read the referee reports and comments from the Associate Editor very carefully. Peer review is inherently a subjective process, but for the most part, paper referees take their jobs very seriously, and there will be valid concerns and comments for improving your manuscript (even if some might not be the most diplomatic when letting you know the faults they find with it!). It may be that the journal you submitted to just might not be the most appropriate venue for your work. Or there may be more substantive changes that are needed to make your manuscript more acceptable for publication.
After my first paper was rejected, I spent a lot of time with my advisor revising it. We eventually re-worked the whole paper (e.g. cutting down the length of the literature review to the most essential points), we proved a new lemma and a new theorem that showed our new estimator’s improvement over previous estimators, and we performed several new simulation studies that showed quite interesting results. We just resubmitted this paper, making appropriate changes suggested by the peer reviewers who had rejected the manuscript, and I have to say my paper was way better than before. The paper was better off in the long-run.
5) The choice of PhD advisor is critical. It's very important that your PhD advisor is someone whom you can have a great working relationship with, whose research is interesting to you personally, and who is actively publishing in respectable journals. I think the last two are more important than anything else, especially for academic jobs. You basically need to have quality papers and excellent recommendation letters if you want to get a good postdoc or faculty position. Some PhD students are hesitant to work with Assistant Professors and are "star-struck" but there's really no point working with a world-renowned professor if their mentorship style and their research does not align with your personal working style/interests. Plus, an Assistant Professor who is actively publishing their work in top journals can still help you develop your career.
Some people need a bit more guidance and an advisor who gently “pushes” them, while others can operate fairly independently and do not need to meet their advisor very frequently. The working style of you and your advisor should mesh well if you hope to be productive.
6) The fields of statistics and biostatistics change very rapidly, so it's more important that you do research that "comes from the heart" than try to keep up with a "hot area." I would not recommend researching a topic that is so archaic and obscure that only a tiny number of people in the world are still working on it. But I also think that you should prioritize your personal interests above what's currently "hot." It can be very difficult to predict what will be "hot" years from now. For example, Dirichlet processes were not very popular when the concept was first introduced, but decades later, Bayesian nonparametrics have exploded in the field of machine learning. It used to be that SVMs were very popular and neural networks lost some of their popularity, but currently, it is the opposite. There is an explosion of interest in neural networks/deep learning and not as much in SVM. The fields of statistics and biostatistics are constantly evolving and changing, so trying to "time" your thesis to a "hot area" can be tricky.
But most importantly, a PhD is a very time-consuming commitment (at least 2 years of research). So you do not want to be miserable the whole time you are doing it. So make sure to pick a thesis topic that you find interesting. You probably won’t be able to do that yourself at first, but to that end, your advisor will help you hone in on some interesting open problems to work on. Do not do a topic that you have no personal interest in! Sure, some people might be more impressed if you do (what they perceive to be) a more "difficult" topic, but at the end of the day, you're the one who has to live with yourself and your career choices. And if your heart just isn't into it, it will make finishing the PhD much more excruciating.   
7) Do not assume that your PhD thesis topic is the only thing you will work on for the rest of your career. To tie in with my previous point, you can always change gears and switch to a “hot” research area after you are done with your PhD. Finishing the PhD is the start of your career and certainly not where you want to peak. A PhD dissertation is usually on a specific, narrow topic or set of topics. Some people are lucky and can milk their research area for the rest of their career, but many people aren't that lucky. 
Even if you want to go into industry, an employer of PhD graduates is going to expect that you can teach yourself new things (new software, new models, etc.) on the fly, even if you've never seen/used these things before. In fact, it is this creativity and ability to learn new things quickly that makes hiring a PhD graduate more appealing than hiring someone with juts a Masters. Likewise in academia, professors are teaching themselves new things and moving into new areas all the time. My own PhD advisor began his career doing frequentist nonparametric statistics, but now he has research in a variety of areas of Bayesian statistics. The postdocs I am currently considering are in entirely new areas that I haven't learned before. By the end of the PhD, you should ideally have enough maturity and initiative to teach yourself different areas of statistics.

Just wanted to give a quick update: I finally got the courage to go to my professor's office to ask him why I haven't heard anything from the department yet. He asked me if I had accepted any offers yet, and I told him that I hadn't yet, but I did have an offer from Berkeley. I also told him that I would choose UChicago over Berkeley in a heartbeat if given the choice. He told me, "I'll see what I can do." Hopefully this is a good sign...

This is a good post with some helpful insights, but I would take issue with some of the things you said in the quoted section above.
First, I don't think that the best indicator of how you will do is to look at past profiles that are most similar to you. While this might be true in theory, in practice people often provide insufficient detail for meaningful comparisons to be made. For example, someone might say that they have published several articles or presented at several conferences, but without knowing the kind of journal or conference they're talking about, it's hard to really gauge how much weight admissions committees gave to this aspect of their application. There is a difference between publishing in JASA or Annals of Statistics vs. an undergraduate journal, between presenting at the top conference in a particular field of statistics vs. presenting at a poster presentation at one's university/college or summer research institution, and between being first author vs. some other author. Similar things can be said about GPA, coursework, and academic awards with respect to rigor. In addition, letters of recommendation can play a big role in the process, but people often describe their letters simply as "very strong," "probably strong," "decent," etc., which isn't very descriptive at all.
I think that a better way to get a sense of how you will fare in the admissions process is to talk to people in your social/academic circles, particularly your professors and any friends who have gone through the process already. Since they are presumably more familiar with your work and with the admissions results of students coming out of your university/college, they are probably in a better position to gauge how you might do.
Second, while I agree that there is some variation in the process, I don't agree that "safety programs really don't exist and lower ranked programs (particularly smaller ones) can be just as hard to get into as large high ranked programs." Of course, I would encourage applicants not to take admission to any particular program as a given. But I think it is fair to say that someone who is competitive for Berkeley or Stanford--assuming that we can know such a thing--probably does not need to apply to as many lower-ranked programs as someone for whom said lower-ranked programs are a match. I think that within broad tiers of schools--say, top 1-30, top 30-50, top 50-100, etc. (I'm just making up the numbers here)--a single applicant can expect some variation in results due to factors like research fit and sheer randomness, but it seems to me that there are clear trends across/between these vaguely defined tiers. In other words, someone who is a match for top 1-30 schools would probably fare better among those schools than someone who is a match for top 50-100 schools. (I seem to recall that some people, historically, would disagree with me about the variation within tiers; they think that if someone is admitted to one top 5-10 school, they will probably be admitted to all or most top 5-10 schools. But I think that these people would agree even more with me, then, about the presence of trends across/between tiers.) Of course, people do sometimes get into their reach schools and rejected from their safety schools; my view is just that it is still meaningful to categorize one's list of schools into reach, match/target, and safety schools, contrary to what was implied in the OP, if the categorization is done well and doesn't just split hairs between, say, the top 5-10 programs. Also, this is only tangentially related, but one should note that acceptance rates are not necessarily a good indicator of admissions competitiveness, since there's a good deal of self-selection among applicants to these schools.

Let us be honest the people on here are not the best people they bring drugs, they bring crime, ... and some, I assume are good people haha

Nvm i got in lmaooo

I've talked with the biostatistics graduate programs coordinator at Minnesota and she had this to say:

"We make no offers of funding to MS or MPH applicants prior to April 15th. Some first year MS/MPH students receive partial funding; most (often all) second year MS students receive at least partial funding. Those decisions are not made until mid-August.  In addition, many of our students go find graduate research positions elsewhere on campus, usually as a statistical programmer, to support them while they complete their Biostat MS/MPH degree. "

I think it's valuable to think about your long term goals here. Keep in mind, your institution and degree program (statistics/biostatistics) will be at the top of your CV for the rest of your life. It will be the first thing every hiring committee sees. That aside, I don't think it's unreasonable to assume that going to a biostatistics program puts you on a track to become faculty in a biostatistics department, and a statistics program points you to positions in statistics departments. Obviously that is not true in every case, but it's a reasonable rule of thumb. I do agree that its easier to go from statistics to biostatistics than the other way around. 
The other key thing to consider, at least about biostatistics, is that it is very applied in practice. To be sure, there are very high profile academic biostatisticians doing fairly theoretical work, but the vast majority of practicing biostatisticians spend a large amount of time providing statistical support to biomedical researchers. Moreover, methods development in biostatistics will require learning a lot about the underlying science. For instance, if you work in statistical genetics, you need to know a lot about genetics. I don't know enough about regular statistics professors to comment on whether they have similar experiences, but my suspicion is there is less of an applied aspect. 
As to the salary issue, let's be precise. Amstat news regularly produces a salary survey and biostat professors report making about $20,000 more than stat professors of equal experience. It is true that the salaries for statistics faculty are for 9 month contracts, compared to 12 months for biostatistics. To be clear, though, a 9 month salary means that stat professors are only contractually obligated to work for 9 months,  but they also aren't paid at all for the remaining 3 months of the year. So at the end of the day, that 9 month salary is what they get in institutional support over 12 months. So the distinction here is that biostatistics faculty are required to work more, but they do in fact get paid more on an annual basis. 
With regard to biostatisticians having to get most of their salary support from grants, that is true, but with some important caveats. Namely, it's easier for biostatisticians to get grants than most other scientists. Basically, the way it works for most biostat faculty is they spend a fairly large amount of time writing the statistics sections of grant applications for biomedical researchers. They get in on enough of these grants that when even a few of them go through, they have salary support. So if working on grant applications does not appeal to you, think very hard about going into biostatistics at an academic institution. However, for those willing, obtaining salary support via grants is not so scary. 

I'll talk about some important factors and fit them into your specific situation.
Research fit:  For someone who is 100% sure they want to do Bayesian statistics, UT Austin is about as good of a place as you can go, besides maybe Duke.  It's really not even close.  TAMU has some great people doing Bayesian statistics too, but it's not the singular focus of the department in the same way.  Mizzou and VaTech are also Bayesian focused, but they can't really compare to the professors at UT.  I'd encourage you to keep an open mind of what you'd like to do  Writing a PhD dissertation in statistics is going to be very math-heavy, and while your thesis might have some applications to social science, you're almost certainly not just going to be able to continue a project you worked on as an undergraduate.

Prestige/rank:  This really fits into research fit, and it's hard to separate the two - how many well-known professors are there that match your research interests?  You'll have no problem getting a job working with many of the top people at UT or TAMU. Mizzou and VaTech have some good Bayesians too, but not in the same quantity and you don't want to put all your eggs in one basket.  Look at the professors who match your research interest, and see how many of them have published recently in top journals like JASA, JRSS, Biometrika, Annals, etc.  If you know you want a faculty job in a PhD-granting statistics department, it's in your best interest to go to UT or TAMU.
Department environment:  Going to UT and being in a cohort of 5 is going to be very different than being in a large cohort at TAMU.  It will be easier to form relationships with faculty in the smaller department, and you'll get more personal attention.  On the other hand, there are less choices for professors to work with, and there are less classmates to form relationships with.  Another thing I would consider is the student demographics.  In a department like Mizzou, it's likely going to be 95%+ students from China.  The sad truth is that it's easier to form friendships with other domestic students, and your first couple years will be easier if you are friends with your classmates.
Location:  You're going to be spending 5 years of your life in this place.  Do you want to spend it in Austin or College Station?  Most people don't even apply to TAMU despite its rank because they can't imagine living there.  This is a personal choice. 
Competitive atmosphere/how hard is the program: I'm not saying you should be choosing the easiest program, but seriously keep this in mind.  A student at a top school might spend 10+ hours a day doing homework, and worry about passing an incredibly difficult qualifying exam after their first year.  For instance, UT has a qualifying exam covering basic first year Casella&Berger level probability (and similar level topics) while TAMU has a more advanced qualifying exam covering measure theory.  Figure out how many (domestic) students get through their quals.
Stipend/Cost of living:  If it's close, this can help make the decision easier.  Yes, it'd be awesome to get the $40k a year fellowship at VaTech over the $20k a year stipend at UT, but keep in mind the big picture.  Personal choice.

I'm a current student, let me clarify. I have guaranteed funding. I expect most other PhD students in the program have guaranteed funding. Even some of the Master's students have funding. I obviously don't know about every student's funding status, so there may a small number of PhD students who do not have it. However, UNC biostatistics absolutely does guarantee funding explicitly. They will send you a letter specifying your exact stipend and the number of years they will guarantee it. As I recall, the funding letter came shortly before the visit day. If you don't get a letter specifying your funding, you don't have guaranteed funding. 
As to the tuition waiver, it's not something to worry about. Your first year funding covers out of state tuition. After the first year, you will have lived in North Carolina long enough to establish residency and therefore will be eligible for in state tuition. Getting in state residency requires submitting a few forms to the state. You be reminded many times by the department to do this.