When a dataset contains forecasts on unscheduled events, such as natural catastrophes, outcomes may be censored or ``hidden'' since some events have not yet occurred. This article finds that this can lead to a selection bias which affects the perceived accuracy and calibration of forecasts. This selection bias can be eliminated by excluding forecasts on outcomes which have been verified surprisingly early.

翻译：当数据集中包含对未预期事件（如自然灾害）的预测时，由于部分事件尚未发生，其结果可能被截断或“隐藏”。本文发现，这可能导致选择偏差，进而影响对预测准确性和校准度的评估。通过排除那些在令人惊讶的早期阶段已得到验证的结果对应的预测，可以消除这种选择偏差。