Unverified50% confidenceSolutionExact time
Practical recommendation: do not abandon stratified sampling just because confidence is uncalibrated; proportional allocation can effectively reduce error under imbalanced distributions.
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50%
Confidence
Long-term
Relevance
10/27/2026
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Related Claims
UnverifiedReduced variance leads to less clipping, because in rejection sampling an overly volatile estimate of the acceptance probability requires more aggressive clipping to ensure numerical stability, and clipping introduces additional distributional bias.80% similarUnverifiedDenser relevance judgments reduce the probability that correct results retrieved by a model are mistakenly judged as irrelevant, thereby improving evaluation reliability.77% similarUnverified多次采样取多数票或自洽性方法可在推理层降低单次随机性对最终结果的影响77% similarVerifiedStatistically, majority voting can effectively reduce error rates only when errors are mutually independent; when errors are highly correlated, consensus instead reinforces mistakes75% similarUnverifiedIn the evaluation context, sampling bias refers to the composition of an evaluation set deviating from the real deployment distribution due to human selection, causing systematic distortion of scores; practices to avoid it include freezing the evaluation set before training, stratified sampling according to the real distribution, and retaining a holdout set that never participates in tuning.75% similar
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