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转载自gradcafe作者:possible_phd 非藤校 (but strong enough) marketing phd
Sobefore anyone points out the obvious, this analysis is of course verybasic and subject to tons of assumptions. haha With that being said, Ican't help but run some numbers through my head with regard to mychances of getting in somewhere this year. I'm thinking about this froma marketing admissions point of view, but it should apply to theothers, as well.
Assume, on average, a 10% chance of admission at any given program ifyou apply across the top 20 or 50 or something. The logic being thatyeah, maybe only 3% get admitted to the very top programs, but figurethat some portion of applicants are not actually competitive becausethey have sub-par stats, don't understand research and want to go toindustry, or for various other reasons. So maybe that bumps theeffective acceptance rate up to 5% or so for applicants that areactually competitive. Apply that same logic to lower ranked schools,and the effect is even more dramatic -- maybe you're so competitiverelative to this pool that you actually have a 20% chance of admissionhere. So on average, 10% is easy enough to work with and might not betoo unreasonable.
If you were to apply to 20 schools, the odds of being rejected by ALLof these programs is then only 12%. That doesn't sound too depressing,right? haha But let's think about this in terms of how we would stackup if we were to rank order applicants and compare ourselves. Let's beconsultants and make even more assumptions! 
If you figure that an average of 2 spots are available per program in,say, the top 30, then there are 60 spots total. So really, you onlyhave to be in the top 60 of applicants that year to get a spot in a top30 PhD program! So how many applicants are there?
Let's assume an average of 350 total applicants per program per year.From a quick look at the breakdowns for Emory and Northwestern, itseems like 15-17% of total PhD applicants are marketing applicants, solet's assume a value of 16%, on average. That works out to an averageof 56 marketing applicants per program per year. For the top 30, thatis a total of 1,680 applications.
However, we all know how PhD admissions work -- people apply EVERYWHERE! So let's take that into account. If we assume the average applicant applies to 9 programs, that meansthe 1,680 applications really only amount to ~187 applicants. So, beingin the top 60 out of 187 applicants only requires you to be in the top32% of these applicants. If you have the requisite GMAT and GPA, goodSOP, and strong LORs, you're very likely to be in that upper third, somaybe we shouldn't freak out so much about the 3-5% acceptance rates,because the odds of us being able to get into a top school are actuallynot too bad. We've just got to be smart about where we apply, sincemost of us will not send out applications to every school in the top 30!
Like I said, I know this logic is very simplified. It assumes the samepool of applicants applies everywhere, that applicants apply to everysingle school in the top 30, etc. But hey, this is just a back of theenvelope calculation, alright? 
他的计算太复杂. 概括一下,想法是这个样子的:
Assume 1. 前30的学校,每个申请者申9个学校; 2. 20% 的申请者背景,分数和能力不qualify, 属于盲申. 3. 前30的学校官方平均录取率为3% p=3%/(1-20%) *9=34% 4. RE, SOP, LOR等优势可以让你站到前33.3%.
推论: 只要条件还不错 (不用完美), 运气还可以, 申请到前30的学校是很容易的. 概率大于34%.
事实上, 他的这个结论只适用于20-30名的学校. 对于前10名 (10-20名)的学校,修改假设如下: 1. 前10 (10-20)的学校,每个申请者拒绝offer的概率为50% (80%); 2. 前10 (10-20)的学校 的申请者有10%的人, 背景,分数和能力不qualify, 属于盲申. 3. 前10 (20) 的学校官方平均录取率为2% (3%) 4. 拥有RE, SOP, LOR等闪光点, 可以排到前50%.
修正: 前10名: Adjusted p=10% 前20名: Adjusted p=32%
现在考虑这样的学生A和B, A 申8所前十的学校; B 申9所10-20的学校
设同批次学校录取概率完全独立, 固知
A 被所有学校所有轮次据的概率: 0.9^8=0.4 B 被所有学校所有轮次据的概率: 0.68^9=0.03
在有闪光点的前提下, 被至少一所前10名的学校录取需要40% 以上的运气 (或者double, triple 闪光点的强度); 被前20名的学校录取只要申的足够多就行
All factors controlled. 以上计算完全从possible_phd大牛同学的推出. All errors are his
补充说明: (看来不得不补充) 1. Just for fun. possible_phd的帖子是为了证明被录取的几率远大于统计上的几率. 2. 计算误差大是因为假设过于简单. 并非学术讨论. 请勿曲解 |
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