Data Science & Analytics
Phack Eval Harness
Run and score the agent p-hacking benchmark. Composes prompt cells across research framing and significance pressure, drives an agent through them, and scores each run into a P-Hacking Intensity index decomposed into selection, search breadth, estimate inflation, inference gap, pre-registration departure and disclosure. Use when benchmarking whether an AI agent p-hacks, comparing models or tool stacks on statistical integrity, scoring a single agent analysis run, or designing an evaluation of statistical guardrails.
- Publisher / source
- brycewang-stanford/Auto-Empirical-Research-Skills (found on GitHub top repositories)
- Upstream record
- brycewang-stanford/Auto-Empirical-Research-Skills
- Version
- Version 1
- Published
- Publication review
- Approved for publication
- Content fingerprint
5832d1bccccca5d42e832920eef34dafd464e9448de232e291872935eee8da5d