data-validationlisted

# Data Validation Skill Pre-delivery QA checklist, common data analysis pitfalls, result sanity checking, and documentation standards for reproducibility. ## Pre-Delivery QA Checklist Run through this checklist before sharing any analysis with stakeholders. ### Data Quality Checks - [ ] **Source verification**: Confirmed which tables/data sources were used. Are they the right ones for this question? - [ ] **Freshness**: Data is current enough for the analysis. Noted the "as of" date. - [ ] **Completeness**: No unexpected gaps in time series or missing segments. - [ ] **Null handling**: Checked null rates in key columns. Nulls are handled appropriately (excluded, imputed, or flagged). - [ ] **Deduplication**: Confirmed no double-counting from bad joins or duplicate source records. - [ ] **Filter verification**: All WHERE clauses and filters are correct. No unintended exclusions. ### Calculation Checks - [ ] **Aggregation logic**: GROUP BY includes all non-aggregated columns. Aggregation level matches the analysis grain. - [ ] **Denominator correctness**: Rate and percentage calculations use the right denominator. Denominators are non-zero. - [ ] **Date alignment**: Comparisons use the same time period length. Partial periods are excluded or noted. - [ ] **Join correctness**: JOIN types are appropriate (INNER vs LEFT). Many-to-many joins haven't inflated counts. - [ ] **Metric definitions**: Metrics match how stakeholders define them. Any deviations are noted. - [ ] **Su