Alexandros Gelastopoulos
@alexgelas.bsky.social
Research fellow at the Institute for Advanced Study in Toulouse. Mathematics in the social sciences.
alexgelas.com
alexgelas.com
There are two possible explanations: 1. distribution for some traits is highly non-gaussian, 2. being average in a trait means less likely to be average in another.
No. 2 would be more interesting. To exclude 1, I would look at the percentage of people within +/-0.25sd for each trait (must be ~20%)
No. 2 would be more interesting. To exclude 1, I would look at the percentage of people within +/-0.25sd for each trait (must be ~20%)
September 10, 2025 at 11:00 AM
There are two possible explanations: 1. distribution for some traits is highly non-gaussian, 2. being average in a trait means less likely to be average in another.
No. 2 would be more interesting. To exclude 1, I would look at the percentage of people within +/-0.25sd for each trait (must be ~20%)
No. 2 would be more interesting. To exclude 1, I would look at the percentage of people within +/-0.25sd for each trait (must be ~20%)
My point is that the only way time enters the picture in Model 2 is through the error terms, which are i.i.d, so this pattern shouldn’t be surprising.
(Disclaimer: I know the answer to the original question.)
(Disclaimer: I know the answer to the original question.)
June 11, 2025 at 6:55 AM
My point is that the only way time enters the picture in Model 2 is through the error terms, which are i.i.d, so this pattern shouldn’t be surprising.
(Disclaimer: I know the answer to the original question.)
(Disclaimer: I know the answer to the original question.)
Interesting. Wouldn't you expect model 2 to (also?) have this property?
June 10, 2025 at 7:52 PM
Interesting. Wouldn't you expect model 2 to (also?) have this property?