 Metacognitive calibration

For correctness indicator function $Y$ and stated probability $C$, ideal calibration means that answers assigned confidence $c$ are correct with conditional probability $c$. Overall overconfidence is $\mathbb{E}[C-Y]>0$; a zero overall difference does not establish calibration at every confidence level. Estimated calibration depends on sample size and how reports are grouped.