A model with R-squared near 0 can still give valid 90% prediction intervals - here's why (and the catch)

I recently calibrated a recovery-rate model that had only two weak features. Its point accuracy was almost nothing — R² basically zero. I expected its uncertainty estimates to be junk too. They weren't: the 90% conformal prediction intervals covered ~89% of held-out outcomes. Valid, just wide . That surprised me enough to nail it down, because it contradicts a belief a lot of us carry around: "my model isn't accurate, so I can't trust its uncertainty." For split conformal prediction, that's back