Data Science & Analytics
Phack Immunization
Make an empirical analysis robust to specification search, before or after the fact. Covers pre-registration and pre-analysis plans encoded as design cards, specification-curve reporting with the Simonsohn joint tests, Romano-Wolf and effective-multiplicity corrections, full-procedure null calibration including for sequential searches, the distance-from-pre-registration diagnostic, split-sample and holdout designs, and the auto-generated honest report. Use when asked how to protect an analysis against p-hacking, how to correct inference after a search has happened, how to write a pre-analysis plan, or how to report a multiverse credibly.
- 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
56148906b401fef5d5e9a842d314fcb18490cd7233c80eff56f9a9f87a7ffdbe