Verified65% confidenceTradeoffExact time
Statistical significance does not equal practical significance; even if the p-value is very small, if the actual improvement is negligible its engineering value remains limited—this corresponds in statistics to the distinction between statistical power and effect size.
3
Sources
65%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
McNemar检验对比机器学习模型:随机种子下的正确用法
redditr/learnmachinelearning7/11/2026
Related Claims
UnverifiedReplacing histograms with per-instance statistics (mean, median, standard deviation) actually slightly degraded performance.74% similarUnverifiedThe comparative experiment did not disclose specific dataset sizes, evaluation metrics, or details of comparison fairness, all of which affect the generalizability of its conclusions.73% similarUnverifiedThis study points out that empirical results on the approximation quality of PFNs are surprisingly scarce.72% similarUnverifiedFor small models with limited parameter counts, data quality can have an even greater impact on final performance than data quantity.71% similarUnverifiedAn increase in relative risk may be negligible in absolute terms if the baseline incidence rate is already very low.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/502215API
curl https://kongchang.com/api/v1/knowledge/claims/502215MCP
get_claim(id=502215)