TXInstrument11

Maybe unusual - but my main goals would be automation and transparency - making the selection more fair, speeding the process up, and saving uncompetitive applicants time & money. 
 
Integrate some kind of survey-esque/Qualtrics-like software that can quickly and neatly divide applicants based on the most relevant stats, such as GPA, GRE, and years of research experience [if most schools have this, they really have no excuse for their slowness]. If sub 3.5 GPAs don't cut the mustard and/or the university has strict GRE score requirements, auto-email all applicants fitting those criteria w/in a week of their submission with a short message explaining just why they were rejected so that they don't reapply next year.  (related to the above) Have such spoken and "unspoken" stats published on the admissions website. (related to the above) Release stats for the previous five years of admits.  If interviews are required, reject all applicants who are not invited to interview IMMEDIATELY. List whether or not faculty are seeking students on their webpages, preferably 2 month in advance of the application deadline. Indicate in instructions whether students should contact faculty or not; make it clear when individual faculty are responsible for accepting students [uT-Austin is, admittedly, very explicit on this front]. If I were a faculty member, have guidelines for exactly what I would want for a prospective to email me (CV + 1 page research statement, etc.) (related to above) Maintain a separate lab email for this purpose Expunge identifying information like name, race, gender, and undergrad/master's university from adcomm's first read-through materials. Add back in uni information and names only after top 5-10% are chosen based on CVs and SOPs. Publish this exact process as well as any other relevant procedures on the website.

I'm currently in a PhD program, and I was forwarded a blog post that I would have found useful as an applicant. It's by a prominent "replication guru", Andrew Gelman.
I am not here to take sides in the replication debate, merely to pass along information that may help you more fully appreciate importance of politics in academia. Gelman's condemnation of a professor's former students is demonstrative. 
http://andrewgelman.com/2016/09/21/what-has-happened-down-here-is-the-winds-have-changed/
While reading this, some troubling rumors I heard about a few departments I applied to suddenly made a lot more sense, as did offhand negative comments I hear routinely from professors in this department. For better or for worse, the popularity of your adviser matters a lot - and arguably more than ever in the current climate.
At my undergrad institution, a practical "no-name" with few power player professors, I only heard whispers of these things from a few select people. If I had better understood the intensity and commonness of these academic cat fights, I might have taken better care in choosing departments to apply to, and I think now that I might have had a better chance of acceptance if I had gone that route by dodging departments that appear to be falling apart at the seams. 
For more examples, check out the feud between Uri Simonsohn and a fellow "replication guru", Greg Francis to see how ugly the mudslinging can get. 

To mirror @lewin's return to the excellent main point of @TXInstrument11, I'll just add a couple of things from my experience.
1) Campus visits prior to deciding which offer to accept can be incredibly helpful. Pay attention to the dynamics between faculty when you're visiting. Ask grad students who is on their committee. If there's someone that seems like they should be on their committee but isn't, ask them why. (Note: I actually did this when visiting a program and found out that two people who you might logically want to put on a committee couldn't stand one another and refused to work with one another's students.) Similarly, ask grad students what they've heard about working with anyone and everyone you're considering working with. 
2) Think seriously and critically about the reputation of the person you're considering having as your PI/advisor. Does that person have a theoretical or methodological bent? If so, do you want to be closely associated with that for the next 10 years of your life? If not, move along to the next POI. 
3) Keep in mind that all of these things can change. People's reputations rise and fall. Feuds get settled and new ones begin. Some of this is out of your control.

Right or wrong, the Replication Movement is causing a lot of academics to tank in popularity. Just because they're right doesn't make it any less political.
Edit: I am part of the Replication Movement so I largely support the mission of people like Gelman, and his confrontational approach is probably what's needed to create change (Cohen, Meehl, & others have been talking about this for ages to no avail, after all), but this is still politics - just between scientists.

There's also: Choose an advisor who doesn't have a reputation for being an asshole. These political debates tarnish reputations on both sides, and my read on the field's sentiment is that it's a lot worse to be someone who rips on others via social media.
A lot of what's going on in social psychology lately reminds me of what's going on with Trump supporters: A minority that feels disenfranchised and embittered, and produces a lot of vitriol and aggression to try and provoke reform from the establishment. Make Science Great Again. 

I'm currently in a PhD program, and I was forwarded a blog post that I would have found useful as an applicant. It's by a prominent "replication guru", Andrew Gelman.
I am not here to take sides in the replication debate, merely to pass along information that may help you more fully appreciate importance of politics in academia. Gelman's condemnation of a professor's former students is demonstrative. 
http://andrewgelman.com/2016/09/21/what-has-happened-down-here-is-the-winds-have-changed/
While reading this, some troubling rumors I heard about a few departments I applied to suddenly made a lot more sense, as did offhand negative comments I hear routinely from professors in this department. For better or for worse, the popularity of your adviser matters a lot - and arguably more than ever in the current climate.
At my undergrad institution, a practical "no-name" with few power player professors, I only heard whispers of these things from a few select people. If I had better understood the intensity and commonness of these academic cat fights, I might have taken better care in choosing departments to apply to, and I think now that I might have had a better chance of acceptance if I had gone that route by dodging departments that appear to be falling apart at the seams. 
For more examples, check out the feud between Uri Simonsohn and a fellow "replication guru", Greg Francis to see how ugly the mudslinging can get. 

Maybe unusual - but my main goals would be automation and transparency - making the selection more fair, speeding the process up, and saving uncompetitive applicants time & money. 
 
Integrate some kind of survey-esque/Qualtrics-like software that can quickly and neatly divide applicants based on the most relevant stats, such as GPA, GRE, and years of research experience [if most schools have this, they really have no excuse for their slowness]. If sub 3.5 GPAs don't cut the mustard and/or the university has strict GRE score requirements, auto-email all applicants fitting those criteria w/in a week of their submission with a short message explaining just why they were rejected so that they don't reapply next year.  (related to the above) Have such spoken and "unspoken" stats published on the admissions website. (related to the above) Release stats for the previous five years of admits.  If interviews are required, reject all applicants who are not invited to interview IMMEDIATELY. List whether or not faculty are seeking students on their webpages, preferably 2 month in advance of the application deadline. Indicate in instructions whether students should contact faculty or not; make it clear when individual faculty are responsible for accepting students [uT-Austin is, admittedly, very explicit on this front]. If I were a faculty member, have guidelines for exactly what I would want for a prospective to email me (CV + 1 page research statement, etc.) (related to above) Maintain a separate lab email for this purpose Expunge identifying information like name, race, gender, and undergrad/master's university from adcomm's first read-through materials. Add back in uni information and names only after top 5-10% are chosen based on CVs and SOPs. Publish this exact process as well as any other relevant procedures on the website.

Maybe unusual - but my main goals would be automation and transparency - making the selection more fair, speeding the process up, and saving uncompetitive applicants time & money. 
 
Integrate some kind of survey-esque/Qualtrics-like software that can quickly and neatly divide applicants based on the most relevant stats, such as GPA, GRE, and years of research experience [if most schools have this, they really have no excuse for their slowness]. If sub 3.5 GPAs don't cut the mustard and/or the university has strict GRE score requirements, auto-email all applicants fitting those criteria w/in a week of their submission with a short message explaining just why they were rejected so that they don't reapply next year.  (related to the above) Have such spoken and "unspoken" stats published on the admissions website. (related to the above) Release stats for the previous five years of admits.  If interviews are required, reject all applicants who are not invited to interview IMMEDIATELY. List whether or not faculty are seeking students on their webpages, preferably 2 month in advance of the application deadline. Indicate in instructions whether students should contact faculty or not; make it clear when individual faculty are responsible for accepting students [uT-Austin is, admittedly, very explicit on this front]. If I were a faculty member, have guidelines for exactly what I would want for a prospective to email me (CV + 1 page research statement, etc.) (related to above) Maintain a separate lab email for this purpose Expunge identifying information like name, race, gender, and undergrad/master's university from adcomm's first read-through materials. Add back in uni information and names only after top 5-10% are chosen based on CVs and SOPs. Publish this exact process as well as any other relevant procedures on the website.

Wow. Let's set the record straight here. I am certainly not stupid enough to think that society is equal and that everything with our education system is all fine and dandy.

While I suggested that details LIKE age, race, and sex be scraped from early rounds of apps, I am not actually opposed to affirmative action-esque policies.

Ironically, my reason for suggesting this was actually about levelling the playing field for those from less well-known schools and to combat favoritism (e.g.for a friend's student/child). Those "good ol' boy network" problems are a much more pressing concern for me than race or gender. Removing identifying information was simply to make that system much harder to maintain. Faculty would have to deliberately game the system by memorizing an applicant's test scores and GPA, and I don't think most would be will to go that far.

Finally, I don't think admissions committees "owe" me anything. This thread was to talk about what we would *ideally* want from an admissions committee in a perfect world. And so what if apps in other domains (job world) are unfair? That doesn't preclude improvement in this one.

Okay, then they can scrap the CV on first pass if those are their priorities. Likewise, if they want to make sure smaller schools get due consideration, they can add that back in. Really, the process can be adjusted for each school, but overall goals of less ID info and the process being more open are better than what we have now. We cannot even imagine how much the factors you listed are or are not being taken into account with how subjective and mysterious it is now.

I also question whether even deliberate moves to include minorities can override first, implicit impressions. Some schools openly acknowledge a policy that low SES and minority is given special consideration and that's great. For other schools who provide no such info, we can only guess.

Besides, at least in the schools I looked at, grad students appeared to be overwhelmingly white and (at least) pseudo-Ivy,so it is debatable how much the current system is helping disadvantaged applicants.

If schools are genuinely committed to eliminating bias, transparency is a good first step - especially since establishing clear standards makes them more accountable for their decisions. I also think it's inexcusable for psych departments, which are very well aware of how bias can cloud selection processes even with the best intentions.

The sarcasm is strong in this one.
 
Incredibly cruel. I would not be surprised if somebody was doing this, judging from the standard operating procedure for several of the schools I applied to [i'm looking at you, NYU and UT-Austin, ya bastards!].

Maybe unusual - but my main goals would be automation and transparency - making the selection more fair, speeding the process up, and saving uncompetitive applicants time & money. 
 
Integrate some kind of survey-esque/Qualtrics-like software that can quickly and neatly divide applicants based on the most relevant stats, such as GPA, GRE, and years of research experience [if most schools have this, they really have no excuse for their slowness]. If sub 3.5 GPAs don't cut the mustard and/or the university has strict GRE score requirements, auto-email all applicants fitting those criteria w/in a week of their submission with a short message explaining just why they were rejected so that they don't reapply next year.  (related to the above) Have such spoken and "unspoken" stats published on the admissions website. (related to the above) Release stats for the previous five years of admits.  If interviews are required, reject all applicants who are not invited to interview IMMEDIATELY. List whether or not faculty are seeking students on their webpages, preferably 2 month in advance of the application deadline. Indicate in instructions whether students should contact faculty or not; make it clear when individual faculty are responsible for accepting students [uT-Austin is, admittedly, very explicit on this front]. If I were a faculty member, have guidelines for exactly what I would want for a prospective to email me (CV + 1 page research statement, etc.) (related to above) Maintain a separate lab email for this purpose Expunge identifying information like name, race, gender, and undergrad/master's university from adcomm's first read-through materials. Add back in uni information and names only after top 5-10% are chosen based on CVs and SOPs. Publish this exact process as well as any other relevant procedures on the website.

Maybe unusual - but my main goals would be automation and transparency - making the selection more fair, speeding the process up, and saving uncompetitive applicants time & money. 
 
Integrate some kind of survey-esque/Qualtrics-like software that can quickly and neatly divide applicants based on the most relevant stats, such as GPA, GRE, and years of research experience [if most schools have this, they really have no excuse for their slowness]. If sub 3.5 GPAs don't cut the mustard and/or the university has strict GRE score requirements, auto-email all applicants fitting those criteria w/in a week of their submission with a short message explaining just why they were rejected so that they don't reapply next year.  (related to the above) Have such spoken and "unspoken" stats published on the admissions website. (related to the above) Release stats for the previous five years of admits.  If interviews are required, reject all applicants who are not invited to interview IMMEDIATELY. List whether or not faculty are seeking students on their webpages, preferably 2 month in advance of the application deadline. Indicate in instructions whether students should contact faculty or not; make it clear when individual faculty are responsible for accepting students [uT-Austin is, admittedly, very explicit on this front]. If I were a faculty member, have guidelines for exactly what I would want for a prospective to email me (CV + 1 page research statement, etc.) (related to above) Maintain a separate lab email for this purpose Expunge identifying information like name, race, gender, and undergrad/master's university from adcomm's first read-through materials. Add back in uni information and names only after top 5-10% are chosen based on CVs and SOPs. Publish this exact process as well as any other relevant procedures on the website.

Maybe unusual - but my main goals would be automation and transparency - making the selection more fair, speeding the process up, and saving uncompetitive applicants time & money. 
 
Integrate some kind of survey-esque/Qualtrics-like software that can quickly and neatly divide applicants based on the most relevant stats, such as GPA, GRE, and years of research experience [if most schools have this, they really have no excuse for their slowness]. If sub 3.5 GPAs don't cut the mustard and/or the university has strict GRE score requirements, auto-email all applicants fitting those criteria w/in a week of their submission with a short message explaining just why they were rejected so that they don't reapply next year.  (related to the above) Have such spoken and "unspoken" stats published on the admissions website. (related to the above) Release stats for the previous five years of admits.  If interviews are required, reject all applicants who are not invited to interview IMMEDIATELY. List whether or not faculty are seeking students on their webpages, preferably 2 month in advance of the application deadline. Indicate in instructions whether students should contact faculty or not; make it clear when individual faculty are responsible for accepting students [uT-Austin is, admittedly, very explicit on this front]. If I were a faculty member, have guidelines for exactly what I would want for a prospective to email me (CV + 1 page research statement, etc.) (related to above) Maintain a separate lab email for this purpose Expunge identifying information like name, race, gender, and undergrad/master's university from adcomm's first read-through materials. Add back in uni information and names only after top 5-10% are chosen based on CVs and SOPs. Publish this exact process as well as any other relevant procedures on the website.

The sarcasm is strong in this one.
 
Incredibly cruel. I would not be surprised if somebody was doing this, judging from the standard operating procedure for several of the schools I applied to [i'm looking at you, NYU and UT-Austin, ya bastards!].

Maybe unusual - but my main goals would be automation and transparency - making the selection more fair, speeding the process up, and saving uncompetitive applicants time & money. 
 
Integrate some kind of survey-esque/Qualtrics-like software that can quickly and neatly divide applicants based on the most relevant stats, such as GPA, GRE, and years of research experience [if most schools have this, they really have no excuse for their slowness]. If sub 3.5 GPAs don't cut the mustard and/or the university has strict GRE score requirements, auto-email all applicants fitting those criteria w/in a week of their submission with a short message explaining just why they were rejected so that they don't reapply next year.  (related to the above) Have such spoken and "unspoken" stats published on the admissions website. (related to the above) Release stats for the previous five years of admits.  If interviews are required, reject all applicants who are not invited to interview IMMEDIATELY. List whether or not faculty are seeking students on their webpages, preferably 2 month in advance of the application deadline. Indicate in instructions whether students should contact faculty or not; make it clear when individual faculty are responsible for accepting students [uT-Austin is, admittedly, very explicit on this front]. If I were a faculty member, have guidelines for exactly what I would want for a prospective to email me (CV + 1 page research statement, etc.) (related to above) Maintain a separate lab email for this purpose Expunge identifying information like name, race, gender, and undergrad/master's university from adcomm's first read-through materials. Add back in uni information and names only after top 5-10% are chosen based on CVs and SOPs. Publish this exact process as well as any other relevant procedures on the website.

my Erdos number (as per http://www.ams.org/mathscinet/collaborationDistance.html)  is 5. i thought it would be less
but i'm much closer to other statisticians that i admire so that's OK
but i always thought this whole "academic lineage" thingy was more of a Physics/Math thingy. i didn't know now all the sciences are jumping into the bandwagon.
now the real question is... WHAT IF PEOPLE END UP DATING THEIR ACADEMIC RELATIVES WITHOUT KNOWING?!?!?

 
 

You need to get your confidence back. Can you do practice interviews with someone so that you feel more comfortable?
 
To answer your question, if UIUC hasn't made an official funding offer yet, then they wouldn't actually be yanking anything. Some programs do admit students that they can't fund, so I'd definitely be on your best behavior if you haven't received a written funding offer prior to your visit. Good luck!

have you considered jumping into the "sharing economy" like becoming an Uber driver or posting on TaskRabbit?
from what i read in one of your other posts (and because i assume we are in somewhat similar programs) i can tell you that if you're willing to do data analysis for other people, you can make a pretty decent buck. and i'm speaking from experience here. god knows i wouldn't have been able to afford graduate school without putting some of my quantitative skills to work

Well, I started off pretty early (3rd year undergrad) because I needed money to finish paying for my degree. At that time there was a website (which I think ended up being bought by freelancer.com) where people would basically just post a brief description of their project, the budget they had available and then people (like me) would bid for those projects. I always took on the social science ones because I knew from my brief stint in psych classes that this is a widely untapped market by people who are mostly familiar STEM areas so I knew I had an angle there.  Little by little I started building up some rep and cut the middle man (the website which takes its share of your money) so I would deal with clients (and referrals) directly. Everything happened online: they would email me a description of their project, I’d give them half of it, I get half of the agreed price on my PayPal and then I’d finish everything for the 2nd half. I can’t tell for sure, but just by the type of questions and project descriptions I got, I’m pretty sure there are a few thesis/dissertations/published articles where all the analysis was done courtesy of yours truly.


 
When Kaggle became available I only kept my best online clients (from which I started building a more “official” looking business as a self-employed data analyst) and devouted my time to Kaggle competitions.  They take a lot of time but even if you only get one right, they pay REALLY well. At the same time, my graduate program opened a position for a “student consultant” to which other graduate students go to looking for advice in terms of running their statistics for their own research or methods questions. And I just straight up started offering: I can either “consult” you for free (because my meagre salary was being paid for the university) or I can do the whole analysis and write up a report with pretty graphs and colours, everything APA style and unlimited follow-ups in case revisions were requested. And from there I just ended up building a client base of students that, as expected, recommended me to their advisors and profs (who have lots of grant money) and then I just starting getting hired by the advisors directly.


 
Overall, the one thing life has taught me is that there is always more data out there than people able to analyze it properly. And if you know how to analyze things (and, more importantly how to effectively communicate the results of your analyses), work (and $$$) will never run out. 

If you are getting any sort of tuition remission or stipend from the university, be very careful to read the fine print - working an outside job during the school year is often explicitly prohibited, and punished by the loss of that aid.

If you want a undergrad class ring and if it will make you happy, then do it Don't worry about what other people do or do not buy

I agree- I didn't buy an undergrad class ring. Instead I bought a necklace from the school bookstore with a school-related charm on it that is distinctive enough that a fellow collegian would notice it but anyone outside of that community would think it is a normal necklace. It's my small way of feeling connected.
 
However you decide to commemorate your time there is your choice and as long as you wouldn't regret the money spent on a ring (I'm not sure how much they go for, but am sure they're probably not cheap), then go for it!

Au contraire, I got a college ring...even had the inner circumference engraved.   
By extension, that means that "all the cool chai_lattes kids did it" and they went overboard w/ engravings!
That's all you need to know.  
Get your ring!!
****
Have I worn mine?  Nope.
Will I ever?  Probably not.  I think my mom has it somewhere in my old room.
But, it's a memento, and I'm glad I got it.

I am surprised at some of the skepticism about getting a MS in Stats here from others, but I would recommend it 110% even if you don't plan on being a researcher. I received my Masters in Quantitative Methods, and worked alongside students in a traditional Stats MS degree program and we took roughly the same classes (at a top tier institution in the Southwest). Now, I did not have to complete 2 years of prerequisites like you are indicating you might have to, but regardless I would say it is still probably worth the investment of time (and it sounds like your program is funded anyways). I can tell you that having a generic MS in Psych will do absolutely nothing for you that the PhD in psych can't already do. Having the MS in Stats will open many doors occupationally and will give you a leg up on future internship/fellowship/job applicants because it is one more tool in your toolbox. I received the same advice a few years ago, coincidentally when I was already in the stats program, and I can tell you that it is a huge benefit to have stats experience. The majority of others in the field you're going into will not understand stats past ANOVAs and regressions, and you would be well-served by gaining this experience.