Statistical Dashboard Integrity and Inference Verification Checklist

Role: Lead Quantitative Researcher with fifteen years of experience in biostatistical computing and analytical dashboard design.

Context

• Target distribution profile: {{dataset_distribution_type}}
• Evaluated metrics: {{key_performance_metrics}}
• Alpha and power thresholds: {{significance_threshold}}
• Dashboard presentation engine: {{dashboard_tool_stack}}
• Research consumer profile: {{target_research_stakeholders}}
• Data refresh cadence: {{data_freshness_latency}}

Task

Deliver an exhaustive, pre-deployment statistical validation checklist that guarantees all mathematical aggregation rules, distribution transformations, hypothesis tests, and visual inferences presented on {{dashboard_tool_stack}} maintain total mathematical rigor for {{target_research_stakeholders}}.

Method

1. Establish validation criteria for underlying baseline assumptions regarding {{dataset_distribution_type}} across all data partitions.
2. Detail verification steps for mathematical formulas driving {{key_performance_metrics}}, separating point estimates from dispersion metrics.
3. Formulate audit items for significance testing displays, ensuring adherence to {{significance_threshold}} and proper multi-comparison corrections.
4. Design edge-case testing checks covering extreme outlier handling, zero-variance subsets, and small sample size anomalies.
5. Specify verification steps for interval estimation visualisations, distinguishing confidence intervals from prediction bands.
6. Detail aggregation checks across disparate time grains influenced by {{data_freshness_latency}}.
7. Map final sign-off criteria against the technical literacy requirements of {{target_research_stakeholders}}.

Constraints

• MUST express every verification step as an actionable binary (Pass/Fail) checkbox item.
• MUST NOT permit ambiguous qualitative criteria without explicit numerical or logical thresholds.
• Mathematical definitions MUST distinguish between population parameters and sample statistics.
• Every metric category must reference specific validation techniques (e.g., bootstrap resampling, parametric tests).
• Include explicit warnings for potential Simpson's Paradox or aggregation bias.

Output format

• Phase 1: Distribution & Data Hygiene Checks (4-6 checklist items with criteria)
• Phase 2: Mathematical & Statistical Metric Audits (6-8 checklist items detailing formula proofs)
• Phase 3: Visual Inference & Hypothesis Testing Verification (4-6 checklist items)
• Phase 4: Boundary, Latency & Edge-Case Protocols (4-5 checklist items)
• Sign-off Matrix (Role-based gatekeeper table)

Self-review

• Confirm all 6 context variables are actively integrated into verification items.
• Verify all checklist items have clear objective acceptance criteria.
• Ensure no generic operational steps replace statistical and mathematical rigor.