Overview
This document is a template, not an empirical study. It demonstrates the registered-report workflow end to end: locking a preregistration, validating its completeness, executing the registered analysis plan against deterministic demonstration data, and reporting confirmatory and exploratory claims through an explicit deviation ledger. Every quantity reported here is produced by the tested code in src/registered_report/ and regenerated by scripts/generate_figures.py; none is hand-entered or illustrative. The demonstration binds a single confirmatory hypothesis (H1) to one registered outcome (primary_score) analysed by a two-sided label-permutation test. Run on a seeded two-group dataset (seed = 20260709, n = 24 per group), the registered test yields an observed mean difference of 1.003 with a two-sided permutation p-value of 0.0005 (0 of 2000 shuffles at least as extreme), significant at the preregistered alpha = 0.05. A deliberately introduced secondary endpoint and an alternative model are carried only as documented deviations, keeping the confirmatory claim boundary intact. The purpose is to give forks a working, auditable skeleton in which planned analyses, frozen hypotheses, deviations, and post-run claims are separable and machine-checkable.
Overview source: Curated paper metadata.
Methods and contributions
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Findings and contributions
- On the synthetic data the registered test gives an observed mean difference of 1.003 and a two-sided permutation p-value of 0.0005, with 0 of 2000 shuffles at least as extreme.
- With both documented deviations, the review packet stays valid, keeps primary_score as the only confirmatory outcome, and reports an integrity score of 0.9.
- The author states that the result says nothing about any real-world phenomenon, because the data are synthetic and the effect is injected by construction.
- The template turns registered-report discipline into code checks, such as a content hash that makes silent edits to the locked plan detectable.
Methods
- Content-hashed registration freeze plus completeness validation — The registration is deep-copied and stamped with a SHA-256 hash over sorted-key JSON, then checked for required sections such as hypotheses, outcomes, exclusion rules, and analysis plan.
- Seeded synthetic two-group dataset (n = 24 per group) — Instead of real data, a seeded generator draws control values from Normal(0, 1) and treatment values from Normal(0.8, 1) to demonstrate the workflow.
- Two-sided label-permutation test with 2000 shuffles and add-one correction — The registered primary model tests the group mean difference at alpha = 0.05 using 2000 seeded label shuffles.
- Deviation ledger classifying executed elements as ok, warning, or error — build_deviation_ledger records each executed outcome and model and grades unregistered elements by whether a documented rationale exists.
- Deliberately plan-divergent demo analysis to exercise the ledger — The demo adds a secondary_score endpoint and swaps the permutation test for a linear model, each with a rationale, to show how deviations are recorded.
Summary sources: Paper metadata and evidence · Extracted source text.
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Citation
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