Welcome! The goal of this blog is to share my analysis of the free, publicly available user-reported law school applicant data from Law School Numbers. Using the data from Law School Numbers is problematic for a variety of reasons (such as users misreporting their actual information, users creating fake accounts, selection bias, etc.) and if I had access to it, I'd much rather work with the data that schools themselves have on applicants. We have what we have, though. Also, while I do have some facility with the type of statistical analysis I employ in my blog posts, I am far from being a professional statistician. I am doing this solely for the purpose of providing my analysis to interested readers, getting feedback, and generating discussion. What I am not doing is prescribing courses of action for law school applicants, or pretending to actually know what goes on behind closed doors in law school admission committees' meetings. I am, however, interested in looking at the story the numbers seem to portray, and sharing that with people with similar interests. I think I'll be able to provide a lot of interesting, and perhaps even helpful, analysis here, but at the end of the day, it is up to the individual law school applicant to put together applications and application strategies tailored to his or her own hopes and goals.

New York University School of Law Profile

Model 1: Waitlists Included Model

In the table below, you will find a list of variables that theoretically play a role in determining whether a law school accepts you or not.  The dataset used in this model includes users on Law School Numbers who reported being either accepted, rejected, or waitlisted at the school in question (those who were first waitlisted and then accepted or rejected are treated as acceptances or rejections, respectively).  For this model, I used an Ordered Logistic Regression, which allows you to look at how each of the independent variables affects the chances that you will be:
      • Accepted rather than (combined waitlisted/rejected)
      • (Combined accepted/waitlisted) rather than rejected
I know, I's a little confusing.  By way of example: in this model for NYU, for two otherwise identical candidates, an additional point on the LSAT increases an applicant's chances of being accepted rather than either waitlisted or rejected by 72.4% (in other words, a 171 is 72.4% more likely than a 170 to get accepted rather than waitlisted or rejected, all else being equal).  A 171 is also 29.3% more likely to get either accepted or waitlisted than rejected than is a 170, all else equal.  If you have any questions, just e-mail me, or check out this link to the awesome UCLA stats site. 

In any case, here are the results from this first model, in which I test the impact of LSAT score, GPA, each earlier month the application is sent (and by earlier month, I don't mean month earlier...I mean September vs. October, or October vs. November), binding early decision, URM status, non-traditional status, and female applicant status.  Everything is based on LSN data from the 2003/2004 cycle to the present.


As you can see, URM candidates get a massive boost in terms of their chances at NYU, being more than forty-five times more likely to get in than an otherwise identical non-URM candidate.  One thing you have to keep in mind when interpreting this is that there are almost certainly different "floors" for LSAT and GPA for URM applicants than non-URM applicants.  This matters because below the numbers "floors" for non-URM applicants, a URM is pretty much infinitely more likely to get in.  The URM "bump" is also significant in terms of LSAT points at 7.0, as well as GPA, at .72 points.  The URM boost is the real story here, but there are statistically significant boosts for earlier applications and female applicants.  The LSAT and GPA boosts are pretty similar.  Conspicuously absent is any discernible advantage for applying via NYU's binding ED application.

Model 2: Waitlists Excluded Model

The next model excludes waitlists, including only applicants that reported either being accepted or rejected (whether that was directly, or after first being waitlisted).  The results in the table should be interpreted in the same way, but the interpretation is a little easier.  The number given for each variable is simply the increase in likelihood of being accepted rather than rejected.


Remember that huge boost for URM applicants from Model 1?  Well in Model 2 it becomes absolutely astronomical.  When we disregard waitlisted applicants for whom we are unsure of their ultimate fate and concentrate on straight acceptances and rejections, URM applicants are more than one-hundred sixty-six times more likely to be accepted than otherwise identical non-URM applicants.  Yes, you read that right.  I double checked it.  Again, the same caveats apply regarding different numbers-floors for each category of applicant, but still...that's impressive.  The LSAT and GPA boosts also increased substantially, and the earlier application and female applicant boosts grew slightly.  Something else that is kind of interesting, although only in a mathematical sense, I suppose, is that while the URM boost almost quadrupled and the LSAT boost only increased by only about 50%, the URM boost in terms of equivalent LSAT points actually shrunk.  Same for GPA equivalent.  Again, no boost for applying binding ED.

Non-Splitters, Splitters, and Reverse-Splitters: Acceptance/WLs/Rejections, and Means

Last, I'm including a table that breaks down how non-splitters, splitters, and reverse-splitters are represented in the data.  This one you really have to be careful with, because the data on LSN does skew towards higher-caliber applicants, and so acceptances are more highly represented than they are in the applicant pool.  Really, the value of this kind of thing will become more clear when we can compare schools, because that same "higher-caliber" applicant caveat will apply across the board, so we can probably draw somewhat valid conclusions by comparing schools.  For now, I'll include it for interested parties, but please do not look at this and say to yourself, "Self, as a splitter I have an X% chance of getting into NYU!"

A relatively high percentage of splitters were accepted (which is typically the case), but it seems that for splitters, that GPA really matters, and if you dip too low, your LSAT won't save you.  For reverse-splitters, however, the GPA is pretty much the same across decision categories, and there is really no difference in the LSAT between accepted and waitlisted candidates (although there is a sharp drop-off for rejected candidates).  Looks like NYU might be willing to hold onto reverse-splitters in case they need those GPAs, but when it comes to pulling reverse-splitters from the waitlist, factors other than numbers play a significant role.

ED vs. RD tables (non-URM and URM applicants)

In the final two tables (which are pretty busy, I realize), I have broken down results and means for non-URM and URM ED and RD applicants.  These are just raw numbers, but definitely bear out the earlier findings on the (lack of) advantage to applying ED.  Take them for what they're worth.

The only place this eyeball-view seems to indicate any kind of advantage to applying ED for non-URM applicants is for non-splitters (applicants with both numbers between the 25th and 75th percentiles).  The problem is that the number of ED applicants is so relatively low that, when you really run the numbers, nothing comes out.  But, for what it's worth, for non-splitter, accepted ED candidates, the mean LSAT was almost two points lower than for accepted regular decision candidates, and the GPA was almost a tenth of a point lower.  Again, when you run the regressions, it comes out as statistically insignificant, but there it is for whatever it's worth.

With URM candidates, the low sample size makes this even more dicey to interpret.  There were absolutely no splitter ED candidates in the data, and only one reverse-splitter ED candidate, so those categories are essentially not worth considering.  There were only 12 non-splitter ED applicants, and again, the means of accepted ED applicants were a little lower than their accepted RD counterparts.


Apply early to NYU to increase your chances of acceptance, and if you are a guy who is just dying to go to NYU at any cost, getting a sex-reassignment surgery might be a better bet than applying ED, especially if you are a splitter or reverse-splitter.  That URM boost, though!

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