I did not fit a distribution to the 25th to 75th range, which is probably what would have suggested a normal distribution for height, and then extrapolated from there. I said I got an annual probability of conflict causing human extinction lower than 10^-9 using 33 or less of the rightmost points of the tail distribution. The 33rd tallest person whose height was recorded was actually 2.42 m, which illustrates I would not have gotten an astronomically low probability for at least 2.42 m.
To be clear, I’m not accusing you of removing outliers from your data. I’m saying that you can’t rule out medium-small probabilities of your model being badly off based on all the direct data you have access to, when you have so few data points to fit your model (not due to your fault, but because reality only gave you so many data points to look at).
My guess is that randomly selecting 1000 data points of human height and fitting a distribution will more likely than not generate a ~normal distribution, but this is just speculation, I haven’t done the data analysis myself.
What do you think is the annualised probability of a nuclear war or volcanic eruption causing human extinction in the next 10 years? Do you see any concrete scenarios where the probability of a nuclear war or volcanic eruption causing human extinction is close to Toby’s values?
I haven’t been able to come up with a good toy model or bounds that I’m happy with, after thinking about it for a bit (I’m sure less than you or Toby or others like Luisa). If you or other commenters have models that you like, please let me know!
(In particular I’d be interested in a good generative argument for the prior).
To be clear, I’m not accusing you of removing outliers from your data. I’m saying that you can’t rule out medium-small probabilities of your model being badly off based on all the direct data you have access to, when you have so few data points to fit your model (not due to your fault, but because reality only gave you so many data points to look at).
My guess is that randomly selecting 1000 data points of human height and fitting a distribution will more likely than not generate a ~normal distribution, but this is just speculation, I haven’t done the data analysis myself.
I haven’t been able to come up with a good toy model or bounds that I’m happy with, after thinking about it for a bit (I’m sure less than you or Toby or others like Luisa). If you or other commenters have models that you like, please let me know!
(In particular I’d be interested in a good generative argument for the prior).