4 ms·

Wish I could find the source, but the math is simple. If you're great (at programming and marketing yourself), you pick where you want to work and apply once (o

Wish I could find the source, but the math is simple. If you're great (at programming and marketing yourself), you pick where you want to work and apply once (or more likely, are convinced to leave one good job for another). If you're good, you pick a few places and apply a handful of times. If you're bad, you end up applying constantly. The average unscreened resume is the average person in the job pool, who is well below the average programmer.

emmett 17y ago
Source: http://www.inc.com/magazine/20070501/column-guest.html?partner=fogcreek http://www.inc.com/magazine/20070501/column-guest.html?partn...
jseliger 17y ago
It's Joel Spolsky. See here: http://joelonsoftware.com/articles/fog0000000050.html http://joelonsoftware.com/articles/fog0000000050.html and http://joelonsoftware.com/articles/FindingGreatDevelopers.html http://joelonsoftware.com/articles/FindingGreatDevelopers.ht...
arghnoname 17y ago
I decided this seems like something one should be able to calculate with conditional probability. Let P(A) be the probability that someone is a pretty good programmer. (I said top ten percent, so .1). P(A') is 1-P(A) = .9 Let P(E) be the probability someone is employed. I guessed and put that at 1-P(E'), guessing programmer unemployment rate at 6%. P(E) = .94 P(E') = .06 Here is a more wild assumption. Let's say that given that someone is a top 10% programmer, there is a 98% probability that he or she is employed. P(E | A) = .98, and conversely, a .02 probability they aren not. With these assumptions in place, I ran the calculations. I was intending to type them up, but it would be difficult with just a text area. Here is the punch line (with those symbols, and assuming I did this correctly, which I did it on the bus on the way home, so probably not)... Given that someone is unemployed, there is a .033 probability that they are in the top 10% and a .96 probability that they are somewhere among the rest. (The 'rest' includes a lot of pretty decent programmers though!) In other words, 3.3% of applicants would be top 10% candidates, and that's assuming equal probability of them sending in an application in the first place. I might rework it with different assumptions. Anyway, you said the math was pretty simple. If someone wants to see how I screwed it up I can type it up into latex and upload it somewhere.

emmett 17y ago

Source: http://www.inc.com/magazine/20070501/column-guest.html?partner=fogcreek http://www.inc.com/magazine/20070501/column-guest.html?partn...

jseliger 17y ago

It's Joel Spolsky. See here: http://joelonsoftware.com/articles/fog0000000050.html http://joelonsoftware.com/articles/fog0000000050.html and http://joelonsoftware.com/articles/FindingGreatDevelopers.html http://joelonsoftware.com/articles/FindingGreatDevelopers.ht...

arghnoname 17y ago

I decided this seems like something one should be able to calculate with conditional probability. Let P(A) be the probability that someone is a pretty good programmer. (I said top ten percent, so .1). P(A') is 1-P(A) = .9 Let P(E) be the probability someone is employed. I guessed and put that at 1-P(E'), guessing programmer unemployment rate at 6%. P(E) = .94 P(E') = .06 Here is a more wild assumption. Let's say that given that someone is a top 10% programmer, there is a 98% probability that he or she is employed. P(E | A) = .98, and conversely, a .02 probability they aren not. With these assumptions in place, I ran the calculations. I was intending to type them up, but it would be difficult with just a text area. Here is the punch line (with those symbols, and assuming I did this correctly, which I did it on the bus on the way home, so probably not)... Given that someone is unemployed, there is a .033 probability that they are in the top 10% and a .96 probability that they are somewhere among the rest. (The 'rest' includes a lot of pretty decent programmers though!) In other words, 3.3% of applicants would be top 10% candidates, and that's assuming equal probability of them sending in an application in the first place. I might rework it with different assumptions. Anyway, you said the math was pretty simple. If someone wants to see how I screwed it up I can type it up into latex and upload it somewhere.