Design and interpret AI evaluations and randomized experiments. Practice uncertainty estimates, A/B assignment checks, prespecified analysis, and transparent re
Last reviewed: 2026-10-03
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.
A/B testing automation should preserve the experiment's design. Define the unit of randomization, primary outcome, analysis window, and stopping rule before collecting results. Automate assignment and data-quality checks, but investigate missing events, unequal exposure, and repeated observations before interpreting a difference as an improvement.
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.