Very interesting. I’m going to try my hand at a short summary:
Assume that you have a number of different options you can choose, that you want to estimate the value of each option and you have to make your best guess as to which option is most valuable. In step one, you generate individual estimates using whatever procedure you think is best. In step 2 you make the final decision, by choosing the option that had the highest estimate in step one.
The point is: even if you have unbiased procedures for creating the individual estimates in step one (ie procedures that are equally likely to overestimate as to underestimate) biases will still be introduced in step 2, when you’re looking at the list of all the different estimates. Specifically, the biases are that the highest estimate(s) are more likely to be overestimates, and the lowest estimate(s) are more likely to be underestimates.
Very interesting. I’m going to try my hand at a short summary:
Assume that you have a number of different options you can choose, that you want to estimate the value of each option and you have to make your best guess as to which option is most valuable. In step one, you generate individual estimates using whatever procedure you think is best. In step 2 you make the final decision, by choosing the option that had the highest estimate in step one.
The point is: even if you have unbiased procedures for creating the individual estimates in step one (ie procedures that are equally likely to overestimate as to underestimate) biases will still be introduced in step 2, when you’re looking at the list of all the different estimates. Specifically, the biases are that the highest estimate(s) are more likely to be overestimates, and the lowest estimate(s) are more likely to be underestimates.