Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.
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Within one week of completing a significant analysis project — while the details are still fresh. Also use after an analysis that went wrong (late delivery, stakeholder rejection, data error discovered post-delivery) to prevent recurrence. Run team retros quarterly even without a specific incident.
references/retro_frameworks.md to stay focused (Start/Stop/Continue or 4Ls: Liked/Lacked/Learned/Longed for).references/learning_capture.md to decide which learnings belong in: templates, reference docs, checklists, or team norms.assets/retrospective_template.md with owners and due dates; log durable learnings in assets/learnings_log_template.md.retrospective_template.md) with what-went-well, issues, root causes, and action itemslearnings_log_template.md) for reusable insightsNo reviews yet. Tell others what you thought.
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Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
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Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
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Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
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Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
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Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.
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Standard business metric calculation with industry benchmarks. Use when calculating SaaS metrics (MRR, churn, LTV, CAC), e-commerce KPIs, or product analytics metrics with proper definitions.
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