JG likely meant “likelihood” in the LW/Bayesian sense, where the proper estimate is the one that is justified by the available evidence at the time of estimation, not the one that is subsequently justified by the way things turned out. It’s often useful to keep those concepts separate.
That’s a good point. I agree with it, and that’s how I try to use the term. Equivalent to your formulation: I was imagining the people who are just like the success stories you’re trying to emulate, who weren’t so lucky—how many of them are there, and what did they lose by trying and failing?
From what I’ve heard, if you fail at a startup, you come out of it with zero net worth and a lot of experience. So you don’t lost much, even if it is not optimally productive.
JG likely meant “likelihood” in the LW/Bayesian sense, where the proper estimate is the one that is justified by the available evidence at the time of estimation, not the one that is subsequently justified by the way things turned out. It’s often useful to keep those concepts separate.
That’s a good point. I agree with it, and that’s how I try to use the term. Equivalent to your formulation: I was imagining the people who are just like the success stories you’re trying to emulate, who weren’t so lucky—how many of them are there, and what did they lose by trying and failing?
From what I’ve heard, if you fail at a startup, you come out of it with zero net worth and a lot of experience. So you don’t lost much, even if it is not optimally productive.
I agree, but I consider the opportunity cost (and stress/sleep/health toll) significant.
Oops. You are right they should be separate. Fixed.