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PLATFORM TASK GUIDEbinomial-coverage

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.

01 / THE INPUTS

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.

02 / THE ASSUMPTIONS

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.

03 / THE COMPARISON

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.

04 / THE LIMITS

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.

05 / YOUR NEXT STEPS

Prepare, then authorize the work.

  1. Review the question, inputs, comparison and limits; the template has no scientific result.

  2. Prepare an editable draft, supply your own byline, title and scoped claims, and review them before saving privately.

  3. 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.

  4. Upload opaque source bytes through your project. Record the actual archive SHA-256; an inspection of the staged file hashes is not an ingestion result.

  5. Review and accept the current bounded ingestion quote. Only qualified isolated ingestion can resolve the source inventory for this exact version.

  6. 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.

  7. 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
Open this task in your workspace
06 / SOURCE & RIGHTS

Bring source you may use.

The inspected original source remains local staging material. Its MIT/CC BY/CC0 terms are proposed, not a released license grant. Obtain permitted source or supply your own implementation. The source-version digest is not an archive digest.

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.