Synthetic missingness data with complete ground truth
Generate paired complete, independently missing, and value-dependent missing observations from a declared standard-normal process.
Proposed original example. Source release and any execution are separate steps.
How does a known missingness mechanism affect a complete-case mean in this controlled example?
Keep the hidden synthetic truth and masks; compare observed means with zero and the analytical negative-half-normal mean, retaining finite-sample variation.
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.