Individual skill

search-procedures

Model how a specification universe is actually walked, rather than assuming it is enumerated. Implements the search procedures a p-hacker or a pressured agent uses (first-significant stopping, random trial within a budget, greedy coordinate descent from the pre-registered analysis, random hill-climbing, and the two-stage split-sample walk with an optional continuation rule, after Adda, Decker and Ottaviani 2020), replays each on null data to measure its false-positive rate and the inflation of what it reports, and records the path so the write-up can be checked against it. Use when asked what a realistic p-hacking session looks like, how likely a given way of searching is to manufacture significance on a specific design, how to calibrate a result that came from a sequential rather than exhaustive search, or to simulate an agent's search behaviour on known-null data.

skills/73-brycewang-p-hacking-skills/skills/09-search-procedures/SKILL.md

Source commit 8684584e7d1127940f018157d2c579974fae8f79

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Matches the observed repository HEAD

Yes

Scanner rules are current

Yes

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No

Observed behavior

Network or broad shell

Last checked

Nov 10, 2026

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