Since the miscalibration metric assesses average calibration it bears no local information. Therefore one can have zero miscalibration area (perfect average calibration) even though e.g. low uncertainties are badly calibrated if the bad local calibration is cancelled by another (opposite) bad calibration at e.g. high uncertainties. Another important aspect of the miscalibration area is the assumption of normal errors. As highlighted by Pernot, such assumptions adds a fragility to the metric, since a non-zero miscalibration area can be interpreted as both a sign that the uncertainties are not calibrated or that the assumption of normal errors was wrong . As an alternative, Pernot suggests the Var(Z) ? = 1 test which makes no assumption on the error distribution. The test consists of calculating a 95% confidence interval of Var(Z) using bootstrap methods and checking whether 1 is part of the interval. For the confidence intervals, we use the BC a method as implemented in SciPy .