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.

Yale Law School 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 Yale, 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 23.8% (in other words, a 173 is 29.3% more likely than a 172 to get accepted rather than waitlisted or rejected, all else being equal).  A 173 is also 23.8% more likely to get either accepted or waitlisted than rejected than is a 172, 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), URM status, non-traditional status, and female applicant status.  Since Yale doesn't have a binding-ED option, I left it off.  Everything is based on LSN data from the 2003/2004 cycle to the present.


URM candidates get a decent boost in terms of their chances at Yale, but nothing even approaching what we see at a lot of schools.  One thing to always 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 pretty big in terms of LSAT points at 7.6 (in other words, a URM candidate gets the same boost for that status as that enjoyed by scoring an additional 7.6 points on the LSAT), but is pretty ho-hum in terms of GPA points, at .29.  This, of course, is also a function of how much of a boost Yale gives for LSAT and GPA, and Yale seems to give more relative weight to the GPA than many (and perhaps most) other schools.  Non-traditional students don't get any type of bump at all, but females are 57.3% more likely to get in than otherwise identical male counterparts. Finally, true to their word, Yale doesn't seem to give any preference to candidates who apply early, so take your time on that application and make sure it's stellar.

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.


Really nothing much to add here, as the results remain basically the same when we just consider acceptances vs. rejections, without considering waitlists (which are problematic both for yield-protection reasons - more prevalent at some schools than others, to be sure - and because a lot of "waitlist" profiles belong to people who didn't bother to update with a final status, so we can't just treat them as rejections).  The numbers boosts get a little bigger, and the female applicant boost gets a little smaller.  One of the reasons there may not be a huge change between models is that Yale just doesn't waitlist all that many people to begin with (and maybe Yale likes to keep a few extra ladies on the waitlist for whatever reason).

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 Yale!"

Something that stands out to me here is that, unlike the vast majority of schools I have looked at so far, based on the LSN reporting, splitters are NOT more likely to get into Yale than are non-splitters or reverse-splitters (the latter being the most frequently shafted group).  Also, look at those means for rejected candidates from all  You can see that, while the predictable trends for LSAT scores and GPAs hold across groups (admits, waitlists, and rejects), that all goes out the window when it comes to splitters or reverse-splitters.

Another takeaway from this, although I'm not including the specific numbers here until I can research to understand them a little better, is that Yale is a lot less numbers based than most of the T-14.  This almost certainly has to do with their unique admissions process.  And, as living proof that it's not all about numbers at Yale, I'd definitely recommend that you put a lot of time and effort into crafting the perfect application, especially since it doesn't seem that getting it in any earlier or later makes a difference.

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