[gsf_emergency_6]

A related* essay (2010) by a statistician on the goals of statistical modelling that I've been procrastinating on:

https://www.stat.berkeley.edu/~aldous/157/Papers/shmueli.pdf

To Explain Or To Predict?

Nice quote

We note that the practice in applied research of concluding that a model with a higher predictive validity is “truer,” is not a valid inference. This paper shows that a parsimonious but less true model can have a higher predictive validity than a truer but less parsimonious model.

Hagerty+Srinivasan (1991)

*like TFA it's a sorta review of Breiman

[0928374082]

is it more than a commentary on overfitting to the tune of "with enough epicycles you can make the elephant wiggle its trunk"?

[gsf_emergency_6]

If you are referring to Hagerty+Srinivasan:

They certainly didn't think that a better fit => "truer".

They used the term "truer" to describe a model that more accurately captures the underlying causal structure or "true" relationship between variables in a population.

As for the paper I linked, I still haven't read it closely enough to confirm that D-Machine's comment below is a good dismissal.

I'm inclined to think it's more like "interpolating vs extrapolating"