How much coverage does a small benchmark have?
For 20 independent trials with success probability 0.05, how often do nominal 95% Wald and Wilson intervals contain the true probability?
This opens the task context in your private workspace. You review an editable plan before saving; your source, permissions, and budget are still required.
What will you examine?
Binomial model: n = 20, p = 0.05, nominal coverage 0.95.
Enumerate success counts 0 through 20; compute both intervals using the 0.975 standard-normal quantile.
A separate descriptive simulation uses 4,000 repetitions and seed 12001 with paired method comparisons.
What does the question assume?
Independent Bernoulli trials share the same fixed success probability.
The exact Wald/Wilson formulas, clipping convention and normal quantile are specified before execution.
What should you compare?
Sum binomial probability over each of the 21 counts whose interval contains p. Full enumeration does not imply exact floating-point arithmetic.
At ideal p = 1/20 the reference membership sets are Wald counts 1–4 and Wilson counts 0–2; compare the recorded interval endpoints and membership before summing.
Retain every count's probability, endpoints and membership, model coverage, simulated coverage, repetition count and seed. Do not fit a pass threshold to the simulation.
What could the evidence establish?
Nominal 95% is a design label; actual model coverage may differ substantially.
The model does not describe correlated benchmark tasks, heterogeneous probabilities or every sample size.
The current finite-statistic checker does not verify interval code or its coverage calculation. Simulation and author-written comparisons remain author observations.
Prepare, then authorize the work.
Review the question, inputs, comparison and limits; the template has no scientific result.
Prepare an editable draft, supply your own byline, title and scoped claims, and review them before saving privately.
Obtain source you may use, or implement the question yourself; select one case and attach your own paper.md in the source ZIP. Do not run submitted code on your host.
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Review and accept the current bounded ingestion quote. Only qualified isolated ingestion can resolve the source inventory for this exact version.
Then review and accept a separate quote for the one author job. Inspect the selected case's status and output, including missing or failed results; an exit code is insufficient.
Any independent predicate, model review, contribution or sharing is a separate request with its own scope, permissions and budget.
Proposed isolated job and expected output paths
Planning details for a separately authorized isolated job. Supply permitted source and review the current quote before execution.
- Job identifier
- binomial-coverage-author
- Runtime
- python-science-v1 · ARM64 · Python standard library · isolated job only
- Command arguments
["python","run_pilot.py","--case","binomial-coverage","--output","results.json"]- Expected output paths
- results.json
Bring source you may use.
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Archive download unavailable. The source remains local staging material with proposed, unreleased license terms.
Inspect the staged file identities
These SHA-256 values identify inspected staging files. They do not grant access to the files or establish a permitted source archive for your version.
- cases.py
- 519571d6d45280e20bd31c73cbb24637a728c36359e61813ba8a3324294fd839
- run_pilot.py
- 609ec673bbd6a83c9795e07aa7b0c1bb70276edf06e2b9e3d1a5da16855eb3ba
- descriptions.json
- 0f5ace321125ae5a0d17802d988c0522e23b63b2774dd66d7c2b02248833381d
- prepare.py
- b492fb44ec02c4a733753e598cebddd2af177598c0dd727606c6baa191cf9ee1
- README.md
- 768e3f3ec31fd0083ca41a1bd9fba7823c54ee18ad21b0e73300e4f6c6c6133d
- MATHEMATICAL_NOTES.md
- a6e041ac742c0b0eee884b1a3a08555faf7957af553f416b761c2ed73f774284
- Staged source version digest
- b36740ef95bae6f480e7c833e97c2aceca73642984015467cff1511392086f88
The source version digest is distinct from an archive checksum. Your uploaded ZIP must have its own exact archive SHA-256.