Unverified50% confidenceTradeoffExact time
When the sample size is small (e.g., only 5–10 successful requests), quantile estimation errors can be large; numpy.percentile or a larger sample size can be used to improve reliability.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe accuracy data in the test has an error margin of approximately ±8–10 percentage points at n=100 sample size (based on a 95% confidence-level binomial confidence interval).79% similarUnverifiedPerformance benchmark results are subject to statistical fluctuation, requiring multiple runs to take the median or P95/P99 percentiles for evaluation.76% similarUnverifiedSetting an absolute failure count ceiling for small-sample slices is more robust than setting a percentage threshold and is insensitive to sample size76% similarUnverifiedPrompt优先方案（用一条超长指令串起数据分析流程）实测成功率不到40%73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/998888API
curl https://kongchang.com/api/v1/knowledge/claims/998888MCP
get_claim(id=998888)