Module 6 — From Data to Auditable Evidence

A credible design can still fail through an undocumented merge, a changed denominator or a figure that cannot be regenerated. Research workflow is therefore part of identification: it preserves the link between the stated assignment process and the analysed records.

Prepare

Choose one result from an earlier module and collect:

• source description and access date;
• code that constructs treatment, outcome and sample;
• software versions and random seed;
• output table or figure;
• interpretation and stated limitation.

If one item cannot be located, that absence is the first audit finding.

Route

1. Data Provenance: trace every analytic field to its source and transformation.
2. Reproducibility and Reporting: make the computational chain rerunnable under public or restricted access.
3. Paper Audit: connect each claim to design, estimate, diagnostic and scope.

Workshop: break and repair a replication

Teams receive a result with one hidden defect: duplicated join keys, post-treatment controls, outcome-driven exclusions, an unseeded split or a mismatched clustering level. They must:

1. reproduce the reported number;
2. locate the defect;
3. quantify its effect;
4. repair code and documentation;
5. rewrite the claim if the conclusion boundary changes.

Follow-up deliverable

Submit a small evidence package with a README, data manifest, deterministic build command, disclosure log and claim–evidence table. Restricted data need not be disclosed unlawfully; provide access instructions, synthetic test data and verifiable code where possible.

Start with Data Provenance