Provenance for precise-looking mock content

These examples and illustrative results are independently authored teaching materials, not measured model results.

Use case

An analytics prototype shows 47.2% growth as fictional composition data. Precision creates factual impressions, so provenance/mock status must accompany the metric visibly.

Mechanism

Inventory numbers/specifications/copy/charts with source/time/definition or mock status. Verify real values’ scope; label unsourced samples where the audience sees them and remove/support published performance claims. Inspect screenshots/exports/alternative text, not just code comments. Refresh disclosure when identity changes.

Bad example

Invent 47.2%, put mock in a comment and present screenshots/public copy as real business growth.

Good example

Without a source, label 47.2% Sample data on card/export and avoid actual-growth claims. For supplied real values verify period/definition and cite. Inspect every number/claim so screenshots distinguish samples.

Why the change matters

Visual precision resembles measured precision. Visible provenance preserves realism without making fiction business evidence.

Observable expectation

Teaching 47.2% has a visible label in screenshot/download; real metrics trace to sources. Removing labels fails content checks. Hidden comments cannot inform image-only readers.

Limits

Mock labels do not prove layout/tests or justify efficacy. Sources may be stale and need time boundaries. Match disclosure to audience and distinguish examples from live production data.