Though Gaussian errors are typically assumed (importantly both NLL and miscalibration area relies on this), we found multiple examples of non-Gaussian error distributions, which can lead to different conclusions for average calibration depending on what metric is looked at (e.g. the miscalibration area (A mis ) or the variance of the Z-score, that is error as a fraction of its uncertainty (Var(Z))).