Rigorous statistical thinking applied to AI system evaluation: experiment design, confidence intervals on eval metrics, cohort analysis, and the causal-inferenc
Statistical analysis for AI is the honesty layer: experimental design for AI features, significance and uncertainty on eval results, bias measurement, and the inferential discipline that separates real improvements from noise dressed as progress.
Rising with eval culture: as AI decisions hinge on measured quality, statistically literate practitioners become the referees, scarce and trusted across engineering and product.
Because eval deltas are noisy: a 2-point quality 'gain' on 50 samples is often nothing. Sample-size and significance discipline prevents shipping noise and reverting wins.
Uncertainty quantification on evaluations: confidence intervals, agreement metrics for judges, and regression tests with defined power. It converts eval culture from vibes to evidence.