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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.07575 |
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| _version_ | 1866915661144391680 |
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| author | Lehmann, Niklas V. |
| author_facet | Lehmann, Niklas V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07575 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Surprisingly-early bias in forecasts for unscheduled events Lehmann, Niklas V. Methodology 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. |
| title | Surprisingly-early bias in forecasts for unscheduled events |
| topic | Methodology |
| url | https://arxiv.org/abs/2512.07575 |