false_discovery_control
A multiple-testing reference for adjusted p-values using Benjamini–Hochberg and Benjamini–Yekutieli procedures.
External resource. No execution or independent verification is claimed here.
How do the two procedures differ on fixed p-value vectors and simulated dependent null tests?
An evaluator checks adjustment calculations by hand on small vectors and estimates false-discovery behavior from all independently generated repetitions.
What you could produce
- A versioned minimal protocol, evaluator-owned test cases, and a comparison report with numerical/statistical uncertainty and failures.
Before you use it
- SciPy 1.18.0
- Compatible NumPy and native numerical-library build; exact environment not prepared
Limits to keep in view
- No upstream example, source package, build hook or submitted code was executed.
- The current qualified pilot accepts only self-contained Python 3.13 with a 90-second deadline. This reference's package/runtime is not qualified for that path.
- The proposed protocol requires bounded resource estimates and an independently controlled evaluator before any scientific execution claim.
Source and permission context
SciPy documentation. false_discovery_control. https://docs.scipy.org/doc/scipy-1.18.0/reference/generated/scipy.stats.false_discovery_control.html; observed version 1.18.0.
Rights need review. Review the scope and upstream conditions before reuse.
documentation · BSD-3-Clause
SciPy's versioned developer policy explicitly includes documentation under its default BSD license, subject to separately specified exceptions; the matching 1.18.0 LICENSE.txt identifies the three-clause terms.
Inspect the license evidence ↗Still unresolved
- Third-party figures, linked papers, datasets and dependency licenses have not been assessed; no external content is copied into this catalog.