While n in principle refers to all errors, it also holds for a suitably large subset, as shown in Figure , and is known as error-based calibration. As we will show in this paper, this is the superior metric for UQ validation. Below follows a more detailed description of how each metric is calculated and its interpretation. ). The errors are divided into bins (here five) according to their uncertainties. The RMSE and root mean variance (RMV) of each bin of errors is calculated (blue dots). For well-calibrated uncertainties (as here), the RMSE vs. RMV plot should follow a straight line with a slope of 1 and an intercept of 0. (b) Error-based calibration for the error-uncertainty distribution in Figure which were scaled to create a mismatch between errors and uncertainties. Unlike the miscalibration area, the error-based calibration metrics catch this mismatch and we see that both linear fit, slope and intercept gets worse.