I disagree about people skills, although my experience with surgeons is that most don’t particularly see the need.
Anyone who operates on the wrong body part or who has a rag in the OR is grossly negligent. This has nothing to do with GPA, MCAT scores or how well they did in med school r in their residencies. It suggests strongly that there may be a substance abuse issue or that the hospital is grossly understaffed forcing physicians to work when they are over tired.
You know, I don't dislike you at all Toni. I'm becoming more and more convinced that we don't even speak the same language. There exists some sort of communication barrier that I just haven't been able to find a way around.
My premise is that good bedside mann
Depends entirely on the distribution around the mean, not just the mean.
Of course.
Let's do a super oversimpified demonstration. Imagine we only have two cohorts, A and B.
We can just use the
2023-24 data from AAMC instead of oversimplified made up numbers.
A has a mean of 50, with a standard deviation of 25. That gives us about 2/3 of the cohort falling between 25 and 75.
B has a mean of 55, with a standard deviation of 10. That gives us 2/3 falling between 45 and 65.
Your hypothetical has difference in μ being significantly bigger than difference in σ. But the MCAT distributions are not like that.
For applicants, Asian MCAT is at a mean of 509.1 with a σ of 9.3.
Black applicants have MCAT at a mean of 497.5 with a σ of 10.0. I.e. standard deviations are similar, but means are not.
If the threshold is set at 60, you will end up with a higher percentage of cohort A being above the threshold than of cohort B, even though B has a higher mean. The same threshold can produce different results based on the standard deviation.
But that is because your distributions have different σs while having similar μs.
Let's apply that to actual data. If we set 500 as cutoff and assume normality, 83.6% of Asian applicants will be above the cutoff, but only 40.1% of blacks will. That's less than half! If we increase the cutoff to 505, 67.0% of Asians are above the cutoff, but only 22.7% of blacks are. That's only about a third. So proportions will change, but the order is not going to flip given these parameters.
Now if you lowered that threshold to 50, you would flip that result - you'd end up with quite a bit more of B in your result than A.
ETA: If you were morbidly inclined, you might consider it score gerrymandering - adjust the aggregate threshold until you get a mix that suits your objectives.
As I have shown, that does not really work unless means are really close and st. devs. are significantly different.
Which is why the admins resort to shady practices like Harvard College assigning Asians poor personality scores in order to limit their numbers.
er is a great thing in a doctor, and makes a doctor better. But bedside manner alone cannot overcome a lack of medical expertise and skill. Medical knowledge is a necessary component of being a good doctor. Someone can be an acceptable doctor without good bedside manner, but cannot be a good doctor without medical knowledge.
I don't think this is a difficult concept, I don't think it's outlandish, and it's certainly not novel. It's not like I'm proposing some new concept here.
What I fail to understand is why you
disagree with me on that very fundamental concept. Enough so that you've disagreed repeatedly, despite me having tried to reframe and re-explain my premise.
Again, undergrad percentage of Asians in class of 2029 at Harvard is 40+%.
You are brain fucking numbers to create an allusion that matches your racist view of collegiate education in the United States.
You don't seem to be looking at the Asian applicants on an individual level when it comes to racism. Here is an example from my neck of the woods. If this isn't racism against Asians, then what is it? Is he just really unlucky? Is it likely a young black or hispanic student with the exact same credentials would experience the same widespread rejection as well?
Northern California high school grad rejected by 16 colleges hired by Google
SAN FRANCISCO -- College admissions decisions disappoint thousands of high-achieving students each year, but one Northern California teen's story is catching the attention of Congress.
Stanley Zhong, 18, is a 2023 graduate of Gunn High School in Palo Alto.
Despite earning 3.97 unweighted and 4.42 weighted GPA, scoring 1590 out of 1600 on the SATs and launching his own e-signing startup
RabbitSign in sophomore year, he was rejected by 16 out of the 18 colleges he applied to.
Although Zhong recognizes that elite college admissions is complicated and his pool of Silicon Valley computer science major applicants is highly competitive, he admits to being surprised.
He was denied by: MIT, Carnegie Mellon, Stanford, UC Berkeley, UCLA, UCSD, UCSB, UC Davis, Cal Poly San Luis Obispo, Cornell University, University of Illinois, University of Michigan, Georgia Tech, Caltech, University of Washington and University of Wisconsin.
His only acceptances: University of Texas and University of Maryland.
College admissions experts frequently tell applicants that schools with an under 5% acceptance rate like MIT and Stanford are reaches for almost everyone, but Zhong was even denied by Cal Poly San Luis Obispo, which has a middle 50% GPA of 4.13-4.25 for admitted engineering students.
Fer Christsake, he was rejected by my alma mater, and I'm apparently just a big dummy (according to many here anyway).
Let me play Devil's Advocate a moment.
With respect to middle tier schools and so-called safety schools in the list, things are different now than they were back in our day applying to colleges. Essentially, safety schools are no longer safe for elite scores etc. There's a thing called Tufts Syndrome (Yield Protection) where elite applications may get waitlisted or even rejected because the probability of attendance is very low. Think a moment from the university's perspective: they need a good method of prediction of how many students will end up attending after they accept them and so if they only take the highest scores, they will be SOL, not to mention, their acceptance rate of admissions will skyrocket, making them look bad, decreasing their revenues in two ways. There is more about this trend
here. You can see from the details on this trend, that it isn't only about skepticism of high scores--there's also looking for signals that the applications are not serious, such as generic college essays, not geared toward the specific school, and other lack of engagement with the school in particular.
Now that this practice by colleges is understood, consider this: it isn't merely that Stanley Zhong was rejected by 16 of the 18 colleges where he applied, it is also that he was accepted by 2 and has chosen not to attend them. So, his applications to safety schools were not too serious to begin with. You may try to counter that his family's lawsuit against universities is very important and this is why he chose not to attend, but his attendance at one of these safety schools is not mutually exclusive with his family's civil lawsuits against universities. The big question here:
since he chose not to attend safety schools he was accepted into, were some of the other safety schools logically correct to exclude him?
Now, as for the elite top-tier schools—places like MIT, Stanford, and Caltech—yield protection isn't the story. At that level, virtually every applicant has an elite SAT score, top GPAs, and high-level AP credit. Once academic threshold requirements are met, these schools differentiate candidates using far more qualitative criteria. When high-stat applicants get rejected at this tier, it usually comes down to something else in their application.
These are criteria and part of the application packet that the colleges have not reported due to FERPA and that for some reason the Zhong family has also not publicized. Among these other factors are college essay, college interview, and teacher recommendations.
Since I am playing Devil's Advocate here, let's focus on two major factors: evaluation mechanics and the distinction between genuine novelty vs. derivative resume-building.
First, consider the granular nature of teacher recommendations. I can attest to how this works for MIT applications specifically. MIT admissions evaluation forms do not just ask if a student is "good"; they ask teachers to rate applicants in specific percentile brackets:
Top 10%,
Top 5%,
Top 1%, or
One of the top 1 or 2 students in my entire career. Coming from an intensely competitive school like Gunn High School, an applicant competes directly against high-achieving classmates for those top-bracket designations. If a teacher ranks a student as "Top 1%" while another applicant from the same school is designated as
one of the best 1 or 2 students ever, that distinction carries enormous weight. It is entirely plausible that a teacher could not check that top box for Zhong because one or two other students in his class--or recent alumni--earned that spot. While elite STEM schools like Caltech or Stanford may use slightly different wording, confidential teacher recommendations remain one of the biggest unseen differentiators in elite admissions.
Second, top research institutions are looking for future leaders on the intellectual frontier, not merely well-oiled cogs or derivative execution. When an elite admissions committee evaluates personal projects, software, or startups, they scrutinize whether the work demonstrates true, disruptive innovation or if it is essentially a well-packaged iteration of existing tech trends. A standard utility app or company--even if functional and tied to a charity model--can easily be perceived by a committee as a polished Silicon Valley resume-builder rather than a breakthrough intellectual pursuit. Elite STEM institutions actively look for candidates who take calculated intellectual risks, tackle fundamental foundational problems, and demonstrate original, "non-linear" thinking. Looking at Zhong's company, RabbitSign: hundreds of e-signing utilities already exist. The underlying engineering, while solid for a high schooler, isn't fundamentally novel and doesn't automatically grant an edge over thousands of equally qualified applicants.
When you combine predictive yield management at mid-tier institutions with the ultra-selective qualitative filtering at top-tier research universities, an application package that looks unbeatable on paper based on a few test scores can easily result in widespread rejections without it being evidence of systemic bias. This is not to say that bias is impossible, but rather that we should keep an open mind until the complete admissions files are disclosed in court. Even then, the
public information flow will be asymmetric: universities remain strictly bound by federal privacy regulations, while plaintiffs can selectively release whatever subset of documents best supports their chosen narrative. So it will be crucial to look at the court's decision and reasoning, but be careful of assuming we have all the information the court had.
Potentially timely analogy: It's all well and good to have your toilet paper bar set at soft, thick, doesn't pill, doesn't tear, doesn't have a nap when supplies are abundant. But when supplies run low, you might very well decide that all you actually care about is whether or not it will block your pipes.
