If we thought that the charity they were switching to were only at the 95th percentile, it could be worse in a log-normal case than a normal case (indeed it could be worse than not getting them to switch).
However that would be an unusual belief for us. More reasonably we might think it were uniformly drawn from the top ten percent (or something). And then log-normal is again much better than normal. I agree with the thrust of your intuition.
Yeah, in GiveWell classic, you’re generally going to estimate that a high-impact charity is on the 95th percentile but with uncertainty around that. Which is in-between the cases that you describe.
I can imagine that knowing that something is on the 95th percentile with high certainty might be worse than guessing that something is on the 95th percentile with high uncertainty, if you have a prior that is some mixture of log-normal, normal and power-law. That’s what we’d have to show to really question the classic GiveWell model.
If we thought that the charity they were switching to were only at the 95th percentile, it could be worse in a log-normal case than a normal case (indeed it could be worse than not getting them to switch).
However that would be an unusual belief for us. More reasonably we might think it were uniformly drawn from the top ten percent (or something). And then log-normal is again much better than normal. I agree with the thrust of your intuition.
Yeah, in GiveWell classic, you’re generally going to estimate that a high-impact charity is on the 95th percentile but with uncertainty around that. Which is in-between the cases that you describe.
I can imagine that knowing that something is on the 95th percentile with high certainty might be worse than guessing that something is on the 95th percentile with high uncertainty, if you have a prior that is some mixture of log-normal, normal and power-law. That’s what we’d have to show to really question the classic GiveWell model.