Eval confidence intervals are too narrow due to clustered data

Binomial confidence intervals for eval pass rates assume independent examples, but most eval sets contain clustered data from shared documents or conversations. This clustering reduces effective sample size, making intervals narrower than they should be. A cluster bootstrap can correct the bias and prevent false conclusions about model improvements.