Bootstrap and Confidence Intervals
Estimating metric uncertainty
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My main metric is [METRIC] calculated on [SAMPLE_SIZE] observations. I want to estimate its confidence intervals without making any distributional assumptions. Explain and implement the bootstrap in Python to calculate 95% CIs, compare with theoretical CIs, and generate the bootstrap distribution visualization.
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Pour des IC bootstrap plus robustes, utilisez le bootstrap *bias-corrected and accelerated* (BCa) via `scipy.stats.bootstrap(..., method='BCa')` plutôt que le percentile simple, surtout si la distribution est asymétrique. Pensez aussi à fixer `random_state` pour la reproductibilité. Si vos observations sont dépendantes (séries temporelles), appliquez un bootstrap par blocs.
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