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.

Stanford 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
By way of example: in this model for Stanford, 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 19.5% (in other words, a 173 is 19.5% more likely than a 172 to get accepted rather than waitlisted or rejected, all else being equal).  A 173 is also 19.5% 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 Stanford 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.


What really jumps out at me here is that Stanford's LSAT boost is considerably smaller than average for a T14 school, while the GPA boost absolutely dwarfs the T14 average, indicating that Stanford places a whole lot more weight on the GPA than it does on the LSAT (which isn't to say the LSAT is unimportant, which a quick glance at Stanford's medians will tell you).  I mean, the numbers here are just really, really lopsided.  URM candidates also get a fantastic boost in terms of their chances at Stanford, being 17.5 times more likely to be accepted than an otherwise identical non-URM candidate.  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 is enormous, at 16.1 (in other words, a URM candidate gets the same boost for that status as that enjoyed by scoring an additional 16.5 points on the LSAT).  This has to be taken, again, bearing in mind the different LSAT and GPA floors for URM vs. non-URM candidates, and how big the URM bump is compared to the relatively small LSAT bump.  To put it as plainly as I can, this 16.5 point LSAT equivalency is not like saying, "Well, if a non-URM with a 3.8 gets in with a 180, a URM with a 3.8 should be able to get in with a 162", because this is almost certainly very unlikely to be the case.  What it really probably means is this: "If a URM with a 3.6 can get in with a 168, a non-URM would need a 184 on the LSAT to get in with a 3.6" which, of course, is impossible.  Neither non-traditional nor female students get any type of bump at all, but for each earlier month that you apply, you increase your chances of admission by 13.5%.

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.


When we drop waitlists and only look at straight accepts or rejects, the boosts just keep getting bigger (and significantly so).  Additionally, in this model, non-traditional students absolutely do get a boost, and a big one at that, being 1.3 times more likely to get in than a traditional student who is otherwise identical.  All the other numbers just get bigger, although the URM boost in terms of its LSAT equivalent shrinks a little, but this is just because the LSAT boost increases more than does the URM boost as we go from model 1 to model 2.  The bump for earlier applications increases pretty significantly.

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 Stanford!"

The big deal here is the lack of luck splitters have, and the relatively better luck reverse-splitters have (both compared to splitters, and also compared to reverse-splitters at other schools).  At almost all other schools I have looked at so far, splitters are slightly more likely to be admitted than are non-splitters, but that is clearly not the case for Stanford.  And almost nowhere are reverse-splitters more likely to get in than are splitters, but that's certainly not true for SLS.  So, again, we get pretty solid evidence that Stanford cares a whole lot more about the applicant's GPA than the applicant's LSAT (although, again, you cannot conclude from this that the LSAT is not extremely important).

If you have a stellar GPA but your LSAT is not quite up to snuff (and by not quite up to snuff, I mean mid-160s, not mid-150s), Stanford might be worth the $100 or so that an application costs, but have that application in tip-top shape and ready to go just as soon as you can to give yourself the best chance.

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