Confidence Interval Methods for Few-Shot AI Evals
IanArawjo · x · 2026-07-16
The author conducted multi-week simulation and replication experiments to determine the best 95% confidence interval method for few-shot AI evaluation data, covering both synthetic and real-world eval data.
The conclusion identifies two methods compared in the text—ER-Tango and Wilson flat—as new adaptation schemes proposed by GPT 5.5 for multi-turn/multi-run data, specifically designed to handle interval estimation challenges in AI eval scenarios.
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