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Five years'. My belief is that you should always email the graduate coordinator to ensure that they've received your supplementary materials; all sorts of things can go wrong with anything that relies on third parties, including the post office or FedEx. Asking "when will I hear back?" is just a nuisance, but you might be able to include the question with a legitimate check-in.

It's what Dr. Falk said when he called me. But my memory might be playing tricks on me; he might have said "about 15" instead. Either way there are still slots left (although I have no idea how good the remaining chances of funding are, if any.)

They were the top 15 candidates, so there will be other admittants, yeah.

I heartily approve of DIY science. I can think of at least three ways to improve your methodology, though: 1) Along with each prediction, state your estimate of the chance that your interpretation of the cards was incorrect. (i.e., given that the Tarot deck has the real power to convey some message, the chance that you correctly guessed what that message is.) 2) Also state the estimated probability, given that Tarot does not work, of the event portrayed in the message occurring, given all the non-Tarot information that was given to you. (I posted a thread earlier providing a rough estimate of the chances that one would get into any schools, for instance, given only the information about what schools an applicant applied to.) If the interpretations you get are likely to be highly idiosyncratic - "if you have good news it will come this week" for one school, "you will not get in" for another - then we may have to throw out predictions whose anterior likelihood is not easily calculable. Your method is to ask for a dream school, whose probability of admission (knowing nothing else) is presumably somewhere around the admissions rate - the dream school is probably the most selective school to which the candidate applied, which should push the chance down, but also a relatively stronger fit for research interests, which should push chances up. What would be ideal would be if, instead of asking for dream school, you asked for a randomly chosen school (research participants could roll a die) and requested that neither the person's profile nor their emotional connection provided information that would be useful for determining whether they got in or not. In that case, if there are no selection effects (Gradcafe users participating in an empirical test of Tarot reading) then the anterior non-supernatural likelihood of admission is just the admissions rate. Of course how many data points you should throw out like this depends on how big your samples are and how many events you can provide with an accurate anterior likelihood. 3) Include a control group: a group of data points composed of false queries and emotional memories. Some quick googling can show you some sites online used to conduct research studies, which may help you raise your sample size.

How would you estimate the chances of a given candidate being admitted to a given school, given (whatever information about the applicant and school that you would like to be considered known?) Well yes, obviously this doesn't attempt to address funding (though you could incorporate it if numbers were available.)

NYU fully funds everyone, if that's any consolation.

So: I chose my POIs and programs based on what I was genuinely interested in (as one would hope.) However, those programs aren't in my undergraduate major (which is, at least, another social science.) I took very few courses in the relevant discipline, and none of my research experience is related (although the skills are transferable.) My SOP emphasized that I've done a lot of independent reading in the subject matter and hopefully conveyed my passion for it, although I'm afraid I didn't sufficiently state what research projects I'd like to undertake, or provide outside evidence of my fit (not that I have it). None of my LORs are from professors in the discipline, although two taught classes related to the subject matter and (I hope) can attest to my passion and independent reading in it. Most of the POIs subscribe to a research program that explicitly disdains disciplinary boundaries, though beyond one case I don't know how many of them are on the adcoms or how much sway they have. I don't want this to be another "will I get into grad school?" thread, which I guess it is, but mostly I'm unclear on how professors measure fit from their end - how they weigh paradigm, substantive interest, and research skill set. From my end I just applied to those who research got me excited, but perhaps I didn't put enough thought into how I'd look from the other side.

To my mind Bread Loaf has been unfairly panned. While it does have a reputation as a "party school" - the undergraduate population is constantly baked - the ranking of its graduate program continues to rise. Academic traditionalists are sure to like its fine marble architecture and (frankly) whitebread demographics, while radicals may appreciate the role various other cultures have played in helping it grow. Don't worry about your funding kneads - its upper-crust benefactors have ensured there's plenty of research dough.

Liberal. Best live music scene in the country. Just the right mix of northern California weird and Texas weird. Bats.

There's no reason to throw away perfectly good political science skills just because the topic is bread. Rational choice theory: this is, in fact, the best loaf of Italian bread you are willing to bake, given the marginal costs of producing a better one. If you valued the product of another baking method more, you would have used that. Classical realism: get used to fucking up your breadbaking, man; it's just part of human nature. Constructivist realism: insufficiently chewy bread is inevitable given the anarchy of the kitchen. Perhaps you should improve its feng shui? Idealism: first, make sure you and the other breadbakers are organized democratically. Then invade the bread and reorganize the yeast along the lines of a parliamentary republic. If you and the bread can't cooperate after that, one of you wasn't really democratic. Critical race theory: what else would you expect? Just like the bread, the reigning ideology adopts a mix of whites and browns on the outside, while remaining just as white - and as dense - as ever in substance. Instrumentalist Marxism: how nice of you to complain about insufficient chewiness when the real purpose of the bread is to poison your neighbor! Structuralist Marxism: wait, stop! I'm sure you don't know this, but the bread is poison! World-Systems theory: asking questions about "the bread" is meaningless. After all, the bread was produced from other things in the kitchen with other things in the kitchen for other things in the kitchen. The proper unit of analysis is the kitchen-system. Positivism: can we really say that your baking the bread in the way you did lead to insufficient chewiness? Keep baking bread just the way you did until we can get the p-value down to to at least .1. Joe Sixpackism: Unenchewment up again??? I say we throw the bâtards out!!

(Actually, there's one case in which GREs give us a very easy calculation: if there's an unofficial math cutoff which, if you make the cutoff, they don't care about.. Since the quant section has an incredibly uniform distribution - one point is very close a quarter-percentile throughout - means equal medians and, if a school's applicant pool acquires its average from being drawn only from the top whatever% of scorers (who are drawn from that group uniformly, at least with respect to GRE scores), an admittant average of x implies a cutoff of 2x-800, such that your chance of being admitted to the program is zero if you're below the cutoff and (admission rate)(800- applicant average)/(800 - admittant average) if you're above. But that's assumption-heavy.)

I think the following toy model might be an unbiased estimator of chances given GPA/GRE - or other factors, for that matter, like number of pubs - on the unlikely chance that you have both the averages for the applicant and admittant pool. Suppose that every applicant has either a low or a high score - say, a 3.5 or a 4.0. (Th average of both pools should be between these scores, and your own score should be equal to or between them.) Then you find what the ratios between applicants must be and the ratios between admittants must be. (If the applicant average is 3.7 and the admittant average is 3.8, then 40% of applicants have high scores, as do 60% of admittants.) Then consider the admissions rate - if it's 20%, say, then that's composed of 12% high scorers and 8% low scorers, meaning that 12/40 (30% of) high scorers and 8/60 (13.3% of) low scorers are admitted. Then give yourself (your score - low score)/(high score - low score) probability that you are in the high-scoring category. So if you have exactly the applicant average your chances remain equal to the admissions rate (as we would expect), and if you have a 3.8, your chances are 0.6*0.4+0.4*.133 = 29%. I'm certain that this model is an unbiased estimator if you really are only measuring a binary factor - if there's gender-based affirmative action (or simple bigotry), say, or measures that are effectively binary, like if you know the percentage of applicants and admittants who are polylingual and either (a) that you're monolingual, (b ) that you speak a typical number of languages for a polyglot (presumably not in this example, since it's probably one-point-something), or © if you speak some number of foreign language and don't have good reason to suspect it's better or worse than the number typical for polyglots (again unlikely, since it's probably one-point-something, but.) For gradients like GPA - and especially the GRE, which has a known and very wonky distribution - my intuition is that something like the toy model above is a good basis for a true unbiased model, but there's some other set of equations, based on what you know otherwise about their distributions, that will tell you 1) what to set as the high and low scores (possibly even ones which are impossible for real students to have) and 2) how to weight your score relative to each, based on something other than p(high)=(yours - low)/(high - low) (possibly weighing one of them more than one and the other negatively). Actually, for a quick proof that your choice of high and low scores matters: suppose we selected a 3.0 and 4.0 for the high and low scores above. In that case 70% of applicants and 80% of admittants have high scores, meaning their 20% admissions rate is composed of 4% low scorers and 16% high scorers, such that 16/70 (22.85% of) high scorers and 4/30 (13.3% of) low scorers are admitted. A cookie to whomever can tell me how to determine the proper high and low scores. (Probably easier cookies: given everything in the OP (or your own, better model) and that you've been accepted into institution Q, what is the conditional probability that you were admitted to institution R? Given that you were rejected from Q? What is your expected number of admissions, given that you will be notified of admissions in an unknown order and then rejections, and that the first notice you receive is admission to Q? Given that you expect to hear admissions in a known order, and then rejections, and that the first notice you receive is admission to Q?)

Out of curiosity, how common are pubs among MAless soc applicants?

Last year people started hearing 18 Februrary. What do you mean by this?

The weasely answer is that, if there aren't distinct trends, rates should be unbiased, and thus so should average admittances. This doesn't work for every function of admission rates, though, unless the distribution is really wonky. I feel like it should baaaaaasically work up until you get admitted somewhere, and then you'd need an idea of what the distribution is, at least if interannual variance is appreciable. If you do know that there's a trend, adjust admission rates by whatever you expect the trend to be (I can't think of any better basis than intuition for its value, but if you're familiar with your field your intuition might be pretty good.) Yeah, this is much more meaningful for those applying to schools that don't offer terminal master's programs, or who are shooting for a PhD but would settle for an MA if offered as consolation prize, &c. If your department offers something qualitatively different like an MFA or MPP it's no good (unless you want to estimate their proportion of the program.) Glad you liked it