data-validation

Survivorship bias is detected by applying methodology checks across your datasets and aggregations to identify skewed populations from hidden selection filters. The QA process inspects analytics artifacts to catch these biases before stakeholders see results.

Aggregation logic validation verifies correct denominators, date alignment, and join correctness across your metrics. Reasonableness checks ensure consistent metric definitions and accurate calculations before generating final reproducible documentation packages.

Reproducible documentation is generated by creating a structured package including data sources, definitions, methodology, assumptions, and versioned queries. This audit-ready output enforces reproducibility and transparency for stakeholder review.

Deduplication issues and null handling errors are identified through data-quality checks that verify source freshness, completeness, and filter logic. The QA process scans datasets to catch these structural data errors before sharing analysis results.

Data validation operates without external dependencies, applying methodology checks directly to your analytics artifacts. It processes datasets, aggregations, and documentation independently to generate structured results packages.