Unverified50% confidenceFactExact time
Large amounts of data from failed trials are permanently lost due to competitive confidentiality, publication bias (journals tend to publish positive results), or company closures.
1
Sources
50%
Confidence
Long-term
Relevance
9/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn data quality validation, the costs of different errors are asymmetric: incorrectly accepting dirty data may cause downstream analysis to break down, while falsely rejecting good data only loses one sample.75% similarUnverifiedData from failure cases has unique value in telling models which paths do not work, and this type of negative information is severely lacking in public literature.71% similarUnverified评估分数依赖测试数据集,数据集随业务变化会导致评分漂移,使历史横向比较产生误导67% similarUnverifiedIn secret scanning scenarios, both false positives and false negatives carry extremely high costs: false negatives may lead to data breaches, while false positives overwhelm security teams with large volumes of invalid alerts67% similarUnverifiedThere is a trade-off between showing the full rejection chain and presenting only the most likely root cause: the full chain is more informative but the report becomes verbose when there are many candidates, while the most likely cause is concise but can mislead users if the judgment is wrong.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/922285API
curl https://kongchang.com/api/v1/knowledge/claims/922285MCP
get_claim(id=922285)