Would you take Tufts, Emory, Wash U over UVA?

[Anonymous]

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:^^^ I don't think you will ever get it. Yes, it is basic probability and statistics. Your data points, the number of graduates enrolled in YLS, and the total number of undergraduates from each undergraduate school, are valid and important. They mean as such as what the metrics are -- the number of graduates enrolled in YLS, and the size of graduates from those colleges.

What you failed to understand is that you divided those two numbers to invent a third metrics and used it to compare "the likelihood of end up at YLS" from each school. That's wrong. Washington U's graduates are NOT 2.5 X as likely to end up at Yale Law as a UVA graduate.

Most sports metrics are derived the same way. Points per game, batting average, 3 point shooting percentage, passing completion percentage. They are then used to compare. Batting average is likelihood of getting a hit per at bat, etc.

No, they are not. 3 point shooting percentage is calculated by # of 3 point shots made divided by 3 point shots attempted.

Likelihood of attending Yale should be calculated by # enrolled/ # applied.

The point was a third metric is created and it is created all the time. I don't think anyone would disagree that it would be great to know admission rate by school, but that isn't available. Beyond that, what would really be ideal to know is, if you could control for all other factors, what the admission rate is by school. By that I mean if the applicants from all schools in the study had identical stats and applications, what would their admission rate be. That would give a better indication of the impact of the school on admissions. It is likely that only Yale Law admissions knows this.

Reports do exactly what the PP did all the time. If you look at Poets and Quants, a site about business schools, they'll talk about "feeder schools" to top Wall Street firms by counting the number of graduates there. Now it could be that graduates of a certain school don't want to work for Goldman Sachs or other firms and don't apply, but we don't really have any information on that. Given that Goldman is prestigious and pays a lot, it isn't a stretch to think a lot of business oriented graduates would like to work there. So the metric has merit in the view of many people and they use it all the time.

Lastly, there are many likelihoods. There is the likelihood a UVA graduate enrolls at Yale Law. There is the likelihood of an applicant from UVA (or another school) being accepted by Yale Law. There is the likelihood of a graduate of a given school being accepted by Yale Law with a specific set of stats (that would allow more precise comparisons). We just need to have progressively more information to know those likelihoods, and it often isn't available.

PP made valid points and provided valuable inputs. I was simply pointing out a math error she made.

You are fine creating as many metrics as you want. But when you use it as a metric of likelihood (probability in statistics), that metric must make mathematical sense.

'Feeder schools' is another fine metric. Ivies are feeder schools to YLS. Washington U (7) and UVA (6) are not, if your cut-off is 10. However, telling people that "Washington U's graduates are 2.5 X likely to end up in Yale Law" is plain wrong.

None of the likelihoods (probability) can use PP's calculation because the formula is wrong mathematically. When you calculate batting average, you divide the number by total hits attempted. When you calculate the likelihood of making a three point shot, you divide the number by the three point shots attempted. When you try to calculate any sort of likelihoods of ending up at YLS, your population must not include students who have absolutely nothing to do with YLS -- graduates never applied to Yale.

[Anonymous]

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:^^^ I don't think you will ever get it. Yes, it is basic probability and statistics. Your data points, the number of graduates enrolled in YLS, and the total number of undergraduates from each undergraduate school, are valid and important. They mean as such as what the metrics are -- the number of graduates enrolled in YLS, and the size of graduates from those colleges.

What you failed to understand is that you divided those two numbers to invent a third metrics and used it to compare "the likelihood of end up at YLS" from each school. That's wrong. Washington U's graduates are NOT 2.5 X as likely to end up at Yale Law as a UVA graduate.

Most sports metrics are derived the same way. Points per game, batting average, 3 point shooting percentage, passing completion percentage. They are then used to compare. Batting average is likelihood of getting a hit per at bat, etc.

No, they are not. 3 point shooting percentage is calculated by # of 3 point shots made divided by 3 point shots attempted.

Likelihood of attending Yale should be calculated by # enrolled/ # applied.

The point was a third metric is created and it is created all the time. I don't think anyone would disagree that it would be great to know admission rate by school, but that isn't available. Beyond that, what would really be ideal to know is, if you could control for all other factors, what the admission rate is by school. By that I mean if the applicants from all schools in the study had identical stats and applications, what would their admission rate be. That would give a better indication of the impact of the school on admissions. It is likely that only Yale Law admissions knows this.

Reports do exactly what the PP did all the time. If you look at Poets and Quants, a site about business schools, they'll talk about "feeder schools" to top Wall Street firms by counting the number of graduates there. Now it could be that graduates of a certain school don't want to work for Goldman Sachs or other firms and don't apply, but we don't really have any information on that. Given that Goldman is prestigious and pays a lot, it isn't a stretch to think a lot of business oriented graduates would like to work there. So the metric has merit in the view of many people and they use it all the time.

Lastly, there are many likelihoods. There is the likelihood a UVA graduate enrolls at Yale Law. There is the likelihood of an applicant from UVA (or another school) being accepted by Yale Law. There is the likelihood of a graduate of a given school being accepted by Yale Law with a specific set of stats (that would allow more precise comparisons). We just need to have progressively more information to know those likelihoods, and it often isn't available.

PP made valid points and provided valuable inputs. I was simply pointing out a math error she made.

You are fine creating as many metrics as you want. But when you use it as a metric of likelihood (probability in statistics), that metric must make mathematical sense.

'Feeder schools' is another fine metric. Ivies are feeder schools to YLS. Washington U (7) and UVA (6) are not, if your cut-off is 10. However, telling people that "Washington U's graduates are 2.5 X likely to end up in Yale Law" is plain wrong.

None of the likelihoods (probability) can use PP's calculation because the formula is wrong mathematically. When you calculate batting average, you divide the number by total hits attempted. When you calculate the likelihood of making a three point shot, you divide the number by the three point shots attempted. When you try to calculate any sort of likelihoods of ending up at YLS, your population must not include students who have absolutely nothing to do with YLS -- graduates never applied to Yale.

OK, how about saying Wash U feeds graduates to Yale Law at 2.5X the rate of UVA (adjusted for undergraduate population)? Does that solve this? I always understood what this meant.

[Anonymous]

Anonymous wrote:UVA boosters can never acknowledge that it is not = to all elite schools in any metric....

Any metric where UVA is favored is a great metric. Any metric where it is not favored is fake news.

[Anonymous]

Anonymous wrote:

Anonymous wrote:UVA boosters can never acknowledge that it is not = to all elite schools in any metric....

Any metric where UVA is favored is a great metric. Any metric where it is not favored is fake news.

This is simply not true. By most metrics we acknowledge that the top 15 or so schools are better. But below that they clearly are not. And when you factor in the cost it’s a hard sell to decline UVA in state over any school not in the top five or so.

[Anonymous]

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:UVA boosters can never acknowledge that it is not = to all elite schools in any metric....

Any metric where UVA is favored is a great metric. Any metric where it is not favored is fake news.

This is simply not true. By most metrics we acknowledge that the top 15 or so schools are better. But below that they clearly are not. And when you factor in the cost it’s a hard sell to decline UVA in state over any school not in the top five or so.

In USNews, Stanford is 7 and Duke is 8. I don't think your logic would make sense at all there. Average salary from U.S. Gov't Scorecard 10 years after graduation is $94K Stanford, $84K Duke, and $61K for UVA.

[Anonymous]

Anonymous wrote:

Anonymous wrote:Cornell?

What about Cornell? OP here. And my son wouldn't get into Cornell. His sister might but they have been flaky with our high school so likely not the ED she will choose.

Outside of engineering, I don't think Cornell is particularly strong and lags in the Ivy League. It does have undergraduate business, like Penn, but isn't nearly as strong as Wharton.

[Anonymous]

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:^^^ I don't think you will ever get it. Yes, it is basic probability and statistics. Your data points, the number of graduates enrolled in YLS, and the total number of undergraduates from each undergraduate school, are valid and important. They mean as such as what the metrics are -- the number of graduates enrolled in YLS, and the size of graduates from those colleges.

What you failed to understand is that you divided those two numbers to invent a third metrics and used it to compare "the likelihood of end up at YLS" from each school. That's wrong. Washington U's graduates are NOT 2.5 X as likely to end up at Yale Law as a UVA graduate.

Most sports metrics are derived the same way. Points per game, batting average, 3 point shooting percentage, passing completion percentage. They are then used to compare. Batting average is likelihood of getting a hit per at bat, etc.

No, they are not. 3 point shooting percentage is calculated by # of 3 point shots made divided by 3 point shots attempted.

Likelihood of attending Yale should be calculated by # enrolled/ # applied.

The point was a third metric is created and it is created all the time. I don't think anyone would disagree that it would be great to know admission rate by school, but that isn't available. Beyond that, what would really be ideal to know is, if you could control for all other factors, what the admission rate is by school. By that I mean if the applicants from all schools in the study had identical stats and applications, what would their admission rate be. That would give a better indication of the impact of the school on admissions. It is likely that only Yale Law admissions knows this.

Reports do exactly what the PP did all the time. If you look at Poets and Quants, a site about business schools, they'll talk about "feeder schools" to top Wall Street firms by counting the number of graduates there. Now it could be that graduates of a certain school don't want to work for Goldman Sachs or other firms and don't apply, but we don't really have any information on that. Given that Goldman is prestigious and pays a lot, it isn't a stretch to think a lot of business oriented graduates would like to work there. So the metric has merit in the view of many people and they use it all the time.

Lastly, there are many likelihoods. There is the likelihood a UVA graduate enrolls at Yale Law. There is the likelihood of an applicant from UVA (or another school) being accepted by Yale Law. There is the likelihood of a graduate of a given school being accepted by Yale Law with a specific set of stats (that would allow more precise comparisons). We just need to have progressively more information to know those likelihoods, and it often isn't available.

PP made valid points and provided valuable inputs. I was simply pointing out a math error she made.

You are fine creating as many metrics as you want. But when you use it as a metric of likelihood (probability in statistics), that metric must make mathematical sense.

'Feeder schools' is another fine metric. Ivies are feeder schools to YLS. Washington U (7) and UVA (6) are not, if your cut-off is 10. However, telling people that "Washington U's graduates are 2.5 X likely to end up in Yale Law" is plain wrong.

None of the likelihoods (probability) can use PP's calculation because the formula is wrong mathematically. When you calculate batting average, you divide the number by total hits attempted. When you calculate the likelihood of making a three point shot, you divide the number by the three point shots attempted. When you try to calculate any sort of likelihoods of ending up at YLS, your population must not include students who have absolutely nothing to do with YLS -- graduates never applied to Yale.

There is nothing wrong with the calculation. It just says something different than you are trying to attribute to it. They were just saying Ivy graduates (regardless of what they want to do, etc.) are more likely to end up enrolled at Yale Law. If you say this is invalid for Washington U due, you are also saying it means nothing when you look at Yale and Harvard, which have 25X as many grads enrolled at Yale Law on a per capita basis. It seems to me there is probably something in that.

[Anonymous]

Anonymous wrote:Having this debate in the household now. Basically, a border line top academic ranked in US News around 20-25 over 25 in US News. Worth double the money? The problem is DS will likely have to ED at one of those and will be committed (full pay) before we find out about UVA. Not sure worth double tuition though.

Which one is your DS most interested in attending? They’re all great schools.

[Anonymous]

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:UVA boosters can never acknowledge that it is not = to all elite schools in any metric....

Any metric where UVA is favored is a great metric. Any metric where it is not favored is fake news.

This is simply not true. By most metrics we acknowledge that the top 15 or so schools are better. But below that they clearly are not. And when you factor in the cost it’s a hard sell to decline UVA in state over any school not in the top five or so.

In USNews, Stanford is 7 and Duke is 8. I don't think your logic would make sense at all there. Average salary from U.S. Gov't Scorecard 10 years after graduation is $94K Stanford, $84K Duke, and $61K for UVA.

This doesn’t mean a lot. For one thing, it could mean that graduates of those other schools are forced to take higher paying jobs that they don’t necessarily want because they have too many student loans. If I’ve seen that once, I’ve seen it 1000 times.

It’s also not the case for my own daughter, who 10 years out from UVA easily was eclipsing six figures.

[Anonymous]

You were doing great until you broke out the embarrassing single data point argument.

[Anonymous]

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:UVA boosters can never acknowledge that it is not = to all elite schools in any metric....

Any metric where UVA is favored is a great metric. Any metric where it is not favored is fake news.

This is simply not true. By most metrics we acknowledge that the top 15 or so schools are better. But below that they clearly are not. And when you factor in the cost it’s a hard sell to decline UVA in state over any school not in the top five or so.

In USNews, Stanford is 7 and Duke is 8. I don't think your logic would make sense at all there. Average salary from U.S. Gov't Scorecard 10 years after graduation is $94K Stanford, $84K Duke, and $61K for UVA.

911! We have a social sciences and basic statistics emergency!!! We need to get this poster a refresher course on cause and effect, extraneous factors, concepts such as cost of living, and more!

[Anonymous]

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:^^^ I don't think you will ever get it. Yes, it is basic probability and statistics. Your data points, the number of graduates enrolled in YLS, and the total number of undergraduates from each undergraduate school, are valid and important. They mean as such as what the metrics are -- the number of graduates enrolled in YLS, and the size of graduates from those colleges.

What you failed to understand is that you divided those two numbers to invent a third metrics and used it to compare "the likelihood of end up at YLS" from each school. That's wrong. Washington U's graduates are NOT 2.5 X as likely to end up at Yale Law as a UVA graduate.

Most sports metrics are derived the same way. Points per game, batting average, 3 point shooting percentage, passing completion percentage. They are then used to compare. Batting average is likelihood of getting a hit per at bat, etc.

No, they are not. 3 point shooting percentage is calculated by # of 3 point shots made divided by 3 point shots attempted.

Likelihood of attending Yale should be calculated by # enrolled/ # applied.

The point was a third metric is created and it is created all the time. I don't think anyone would disagree that it would be great to know admission rate by school, but that isn't available. Beyond that, what would really be ideal to know is, if you could control for all other factors, what the admission rate is by school. By that I mean if the applicants from all schools in the study had identical stats and applications, what would their admission rate be. That would give a better indication of the impact of the school on admissions. It is likely that only Yale Law admissions knows this.

Reports do exactly what the PP did all the time. If you look at Poets and Quants, a site about business schools, they'll talk about "feeder schools" to top Wall Street firms by counting the number of graduates there. Now it could be that graduates of a certain school don't want to work for Goldman Sachs or other firms and don't apply, but we don't really have any information on that. Given that Goldman is prestigious and pays a lot, it isn't a stretch to think a lot of business oriented graduates would like to work there. So the metric has merit in the view of many people and they use it all the time.

Lastly, there are many likelihoods. There is the likelihood a UVA graduate enrolls at Yale Law. There is the likelihood of an applicant from UVA (or another school) being accepted by Yale Law. There is the likelihood of a graduate of a given school being accepted by Yale Law with a specific set of stats (that would allow more precise comparisons). We just need to have progressively more information to know those likelihoods, and it often isn't available.

PP made valid points and provided valuable inputs. I was simply pointing out a math error she made.

You are fine creating as many metrics as you want. But when you use it as a metric of likelihood (probability in statistics), that metric must make mathematical sense.

'Feeder schools' is another fine metric. Ivies are feeder schools to YLS. Washington U (7) and UVA (6) are not, if your cut-off is 10. However, telling people that "Washington U's graduates are 2.5 X likely to end up in Yale Law" is plain wrong.

None of the likelihoods (probability) can use PP's calculation because the formula is wrong mathematically. When you calculate batting average, you divide the number by total hits attempted. When you calculate the likelihood of making a three point shot, you divide the number by the three point shots attempted. When you try to calculate any sort of likelihoods of ending up at YLS, your population must not include students who have absolutely nothing to do with YLS -- graduates never applied to Yale.

There is nothing wrong with the calculation. It just says something different than you are trying to attribute to it. They were just saying Ivy graduates (regardless of what they want to do, etc.) are more likely to end up enrolled at Yale Law. If you say this is invalid for Washington U due, you are also saying it means nothing when you look at Yale and Harvard, which have 25X as many grads enrolled at Yale Law on a per capita basis. It seems to me there is probably something in that.

There is nothing right with that calculation of likelihood. She divided the # of enrolled by the total # of undergraduates whether they applied to Yale or not, and used it as a measure of your likelihood of ending up at Yale.

You don't need to "adjust" for the total number of undergraduate graduates to come to the conclusion that Ivies are feeder schools to Yale Law, and UVA and Washington are not, just by looking at the number of enrolled per school. How many times likely does an Ivy graduate end up at Yale as a UVA or Washington graduate? We simply don't have data to calculate that.

Poets and Quants reports top feeder schools to Wall Street. Do they have a metric that divides their numbers by the total undergraduate population? No.

[Anonymous]

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:^^^ I don't think you will ever get it. Yes, it is basic probability and statistics. Your data points, the number of graduates enrolled in YLS, and the total number of undergraduates from each undergraduate school, are valid and important. They mean as such as what the metrics are -- the number of graduates enrolled in YLS, and the size of graduates from those colleges.

What you failed to understand is that you divided those two numbers to invent a third metrics and used it to compare "the likelihood of end up at YLS" from each school. That's wrong. Washington U's graduates are NOT 2.5 X as likely to end up at Yale Law as a UVA graduate.

Most sports metrics are derived the same way. Points per game, batting average, 3 point shooting percentage, passing completion percentage. They are then used to compare. Batting average is likelihood of getting a hit per at bat, etc.

No, they are not. 3 point shooting percentage is calculated by # of 3 point shots made divided by 3 point shots attempted.

Likelihood of attending Yale should be calculated by # enrolled/ # applied.

The point was a third metric is created and it is created all the time. I don't think anyone would disagree that it would be great to know admission rate by school, but that isn't available. Beyond that, what would really be ideal to know is, if you could control for all other factors, what the admission rate is by school. By that I mean if the applicants from all schools in the study had identical stats and applications, what would their admission rate be. That would give a better indication of the impact of the school on admissions. It is likely that only Yale Law admissions knows this.

Reports do exactly what the PP did all the time. If you look at Poets and Quants, a site about business schools, they'll talk about "feeder schools" to top Wall Street firms by counting the number of graduates there. Now it could be that graduates of a certain school don't want to work for Goldman Sachs or other firms and don't apply, but we don't really have any information on that. Given that Goldman is prestigious and pays a lot, it isn't a stretch to think a lot of business oriented graduates would like to work there. So the metric has merit in the view of many people and they use it all the time.

Lastly, there are many likelihoods. There is the likelihood a UVA graduate enrolls at Yale Law. There is the likelihood of an applicant from UVA (or another school) being accepted by Yale Law. There is the likelihood of a graduate of a given school being accepted by Yale Law with a specific set of stats (that would allow more precise comparisons). We just need to have progressively more information to know those likelihoods, and it often isn't available.

PP made valid points and provided valuable inputs. I was simply pointing out a math error she made.

You are fine creating as many metrics as you want. But when you use it as a metric of likelihood (probability in statistics), that metric must make mathematical sense.

'Feeder schools' is another fine metric. Ivies are feeder schools to YLS. Washington U (7) and UVA (6) are not, if your cut-off is 10. However, telling people that "Washington U's graduates are 2.5 X likely to end up in Yale Law" is plain wrong.

None of the likelihoods (probability) can use PP's calculation because the formula is wrong mathematically. When you calculate batting average, you divide the number by total hits attempted. When you calculate the likelihood of making a three point shot, you divide the number by the three point shots attempted. When you try to calculate any sort of likelihoods of ending up at YLS, your population must not include students who have absolutely nothing to do with YLS -- graduates never applied to Yale.

There is nothing wrong with the calculation. It just says something different than you are trying to attribute to it. They were just saying Ivy graduates (regardless of what they want to do, etc.) are more likely to end up enrolled at Yale Law. If you say this is invalid for Washington U due, you are also saying it means nothing when you look at Yale and Harvard, which have 25X as many grads enrolled at Yale Law on a per capita basis. It seems to me there is probably something in that.

There is nothing right with that calculation of likelihood. She divided the # of enrolled by the total # of undergraduates whether they applied to Yale or not, and used it as a measure of your likelihood of ending up at Yale.

You don't need to "adjust" for the total number of undergraduate graduates to come to the conclusion that Ivies are feeder schools to Yale Law, and UVA and Washington are not, just by looking at the number of enrolled per school. How many times likely does an Ivy graduate end up at Yale as a UVA or Washington graduate? We simply don't have data to calculate that.

Poets and Quants reports top feeder schools to Wall Street. Do they have a metric that divides their numbers by the total undergraduate population? No.

So if Amherst (with about 2,000 undergraduate students) has the same number of Rhodes Scholars, Yale Law acceptances, or Nobel Prize winners among its graduates as Michigan (30,000 undergraduate students), it wouldn't be more productive by any measure? Really?

[Anonymous]

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:

Anonymous wrote:^^^ I don't think you will ever get it. Yes, it is basic probability and statistics. Your data points, the number of graduates enrolled in YLS, and the total number of undergraduates from each undergraduate school, are valid and important. They mean as such as what the metrics are -- the number of graduates enrolled in YLS, and the size of graduates from those colleges.

What you failed to understand is that you divided those two numbers to invent a third metrics and used it to compare "the likelihood of end up at YLS" from each school. That's wrong. Washington U's graduates are NOT 2.5 X as likely to end up at Yale Law as a UVA graduate.

Most sports metrics are derived the same way. Points per game, batting average, 3 point shooting percentage, passing completion percentage. They are then used to compare. Batting average is likelihood of getting a hit per at bat, etc.

No, they are not. 3 point shooting percentage is calculated by # of 3 point shots made divided by 3 point shots attempted.

Likelihood of attending Yale should be calculated by # enrolled/ # applied.

The point was a third metric is created and it is created all the time. I don't think anyone would disagree that it would be great to know admission rate by school, but that isn't available. Beyond that, what would really be ideal to know is, if you could control for all other factors, what the admission rate is by school. By that I mean if the applicants from all schools in the study had identical stats and applications, what would their admission rate be. That would give a better indication of the impact of the school on admissions. It is likely that only Yale Law admissions knows this.

Reports do exactly what the PP did all the time. If you look at Poets and Quants, a site about business schools, they'll talk about "feeder schools" to top Wall Street firms by counting the number of graduates there. Now it could be that graduates of a certain school don't want to work for Goldman Sachs or other firms and don't apply, but we don't really have any information on that. Given that Goldman is prestigious and pays a lot, it isn't a stretch to think a lot of business oriented graduates would like to work there. So the metric has merit in the view of many people and they use it all the time.

Lastly, there are many likelihoods. There is the likelihood a UVA graduate enrolls at Yale Law. There is the likelihood of an applicant from UVA (or another school) being accepted by Yale Law. There is the likelihood of a graduate of a given school being accepted by Yale Law with a specific set of stats (that would allow more precise comparisons). We just need to have progressively more information to know those likelihoods, and it often isn't available.

PP made valid points and provided valuable inputs. I was simply pointing out a math error she made.

You are fine creating as many metrics as you want. But when you use it as a metric of likelihood (probability in statistics), that metric must make mathematical sense.

'Feeder schools' is another fine metric. Ivies are feeder schools to YLS. Washington U (7) and UVA (6) are not, if your cut-off is 10. However, telling people that "Washington U's graduates are 2.5 X likely to end up in Yale Law" is plain wrong.

None of the likelihoods (probability) can use PP's calculation because the formula is wrong mathematically. When you calculate batting average, you divide the number by total hits attempted. When you calculate the likelihood of making a three point shot, you divide the number by the three point shots attempted. When you try to calculate any sort of likelihoods of ending up at YLS, your population must not include students who have absolutely nothing to do with YLS -- graduates never applied to Yale.

There is nothing wrong with the calculation. It just says something different than you are trying to attribute to it. They were just saying Ivy graduates (regardless of what they want to do, etc.) are more likely to end up enrolled at Yale Law. If you say this is invalid for Washington U due, you are also saying it means nothing when you look at Yale and Harvard, which have 25X as many grads enrolled at Yale Law on a per capita basis. It seems to me there is probably something in that.

There is nothing right with that calculation of likelihood. She divided the # of enrolled by the total # of undergraduates whether they applied to Yale or not, and used it as a measure of your likelihood of ending up at Yale.

You don't need to "adjust" for the total number of undergraduate graduates to come to the conclusion that Ivies are feeder schools to Yale Law, and UVA and Washington are not, just by looking at the number of enrolled per school. How many times likely does an Ivy graduate end up at Yale as a UVA or Washington graduate? We simply don't have data to calculate that.

Poets and Quants reports top feeder schools to Wall Street. Do they have a metric that divides their numbers by the total undergraduate population? No.

So if Amherst (with about 2,000 undergraduate students) has the same number of Rhodes Scholars, Yale Law acceptances, or Nobel Prize winners among its graduates as Michigan (30,000 undergraduate students), it wouldn't be more productive by any measure? Really?

Yes. The people of Switzerland are poor because the country only has a GDP of $680B vs $19 trillion in the U.S.

[Anonymous]

Anonymous wrote:You were doing great until you broke out the embarrassing single data point argument.

Yea, you’re probably right. It’s true though.