Conditional moments of a bootstrap mean generator
Resample a fixed twenty-value empirical distribution and compare bootstrap moments with their conditional analytical values.
Proposed original example. Source release and any execution are separate steps.
How do observed bootstrap moments compare with their conditional expectation and variance?
The bootstrap mean's conditional expectation is the empirical mean and its variance is empirical population variance divided by sample size; no population interval-coverage claim is made.
What you could produce
- A scoped observations JSON record and a comparison with the stated reference.
Before you use it
- Python 3.13 standard library
Limits to keep in view
- No research code was executed by the preparation tool.
- Author output cannot issue an independent scientific-verification result.
Source and permission context
Original local preparation by the Executable Science seed collection; upstream API references remain separately attributed.
Catalog listing reviewed. This review covers the description and source links displayed here.
Local draft title, collective byline, original summary, and proposed split terms reviewed.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
Before copying source material
- Proposed local-draft licenses: original code MIT, explanations CC-BY-4.0, synthetic numeric data CC0-1.0; publication/disclosure approval remains separate.