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Luck eventually runs out. That's not to say you're wrong, only that I expect the distribution of the lucky amongst the skilled tends towards zero over sufficien

Luck eventually runs out. That's not to say you're wrong, only that I expect the distribution of the lucky amongst the skilled tends towards zero over sufficient time. "Beware old men in a young man's game."

Retric 4y ago
Depends on how often you role the dice not just how bad the dice are. Someone that’s got a 1% chance of dying every fireworks show is extraordinarily bad at their job. But if they only put on 2 shows a year they have better than even odds of surviving a 40 year career.
- somenameforme 4y ago
  The odds there are surprisingly high, but not better than even. 44.75%
  - GreymanTheGrey 4y ago
    Actually the OP is correct - the odds of dying are 44.75%, while the odds of surviving are the corollary of 55.25%, i.e. greater than even. Edit: Nope I'm wrong, just redid the math. Apologies.
  - bawolff 4y ago
    But the important odds in this situation is not the individual, but the group - if you start with a group of 100 (or whatever) people extraordinarily bad at their job, what is the probability at least 1 makes it 40 years. Survivorship bias is a bias because the sample is biased - Its when you pick a group of people who have already won and then are surprised that they beat the odds and won because you confuse the probability that a specific person wins with the probability that at least 1 person wins.
    - dmurray 4y ago
      Actually, the important odds are: given that you've met a 75-year old explosives professional, how likely is it that he was skilful instead of lucky? To get a good estimate of this you need to come up with some priors about how many skilful and unskillful equivalents there were at the age of 30 and how much worse the unskillful ones are at staying alive.
  - Retric 4y ago
    It was a survivorship bias point but I didn’t make that clear. I meant if you meet X people at a random point in their career then on average more than half will make it to the end because some of the original sample died before you sampled the population. Basically older people would have a lower chance of their luck running out even if they where all equally incompetent. At least assuming the original population isn’t dying of something else vastly more often etc.
bawolff 4y ago
The real question is what amount of time is sufficient. Its probably longer than you expect.

Retric 4y ago

Depends on how often you role the dice not just how bad the dice are. Someone that’s got a 1% chance of dying every fireworks show is extraordinarily bad at their job. But if they only put on 2 shows a year they have better than even odds of surviving a 40 year career.

somenameforme 4y ago
The odds there are surprisingly high, but not better than even. 44.75%
- GreymanTheGrey 4y ago
  Actually the OP is correct - the odds of dying are 44.75%, while the odds of surviving are the corollary of 55.25%, i.e. greater than even. Edit: Nope I'm wrong, just redid the math. Apologies.
- bawolff 4y ago
  But the important odds in this situation is not the individual, but the group - if you start with a group of 100 (or whatever) people extraordinarily bad at their job, what is the probability at least 1 makes it 40 years. Survivorship bias is a bias because the sample is biased - Its when you pick a group of people who have already won and then are surprised that they beat the odds and won because you confuse the probability that a specific person wins with the probability that at least 1 person wins.
  - dmurray 4y ago
    Actually, the important odds are: given that you've met a 75-year old explosives professional, how likely is it that he was skilful instead of lucky? To get a good estimate of this you need to come up with some priors about how many skilful and unskillful equivalents there were at the age of 30 and how much worse the unskillful ones are at staying alive.
- Retric 4y ago
  It was a survivorship bias point but I didn’t make that clear. I meant if you meet X people at a random point in their career then on average more than half will make it to the end because some of the original sample died before you sampled the population. Basically older people would have a lower chance of their luck running out even if they where all equally incompetent. At least assuming the original population isn’t dying of something else vastly more often etc.

bawolff 4y ago

The real question is what amount of time is sufficient. Its probably longer than you expect.