Two things I think are interesting here, one discussed by the author and one not. (1) As mentioned at the bottom, forecasting usually should lead to decisionmaking, and when it gets disconnected, it can be unclear what the value is. It sounds like Rosenfield is trying to use forecasting to give added weight to his statistical conclusions about past data, which I agree sounds suspect.
(2) it's not clear what the "error bars" should mean. One is a confidence interval[1] (e.g. model gives 95% chance the output will be within these bounds). Another is a standard deviation (i.e. you are pretty much predicting the squared difference between your own point forecast and the outcome).
(2) it's not clear what the "error bars" should mean. One is a confidence interval[1] (e.g. model gives 95% chance the output will be within these bounds). Another is a standard deviation (i.e. you are pretty much predicting the squared difference between your own point forecast and the outcome).
[1] acknowledged: not the correct term