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wushu/.trae/skills/planning-with-files/templates/analytics_findings.md
2026-03-30 02:35:31 +08:00

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# Findings & Decisions
<!--
WHAT: Knowledge base for your analytics session. Stores data sources, hypotheses, and results.
WHY: Context windows are limited. This file is your "external memory" for analytical work.
WHEN: Update after ANY discovery, especially after running queries or viewing charts.
-->
## Data Sources
<!--
WHAT: Every data source you connected to, with schema details and quality notes.
WHY: Knowing where your data came from and its limitations is critical for reproducibility.
EXAMPLE:
| user_events | PostgreSQL prod replica | 2.3M rows | user_id, event_type, ts | 0.2% null user_id |
| revenue.csv | Finance team export | 45K rows | account_id, mrr, churn_date | Complete, no nulls |
-->
| Source | Location | Size | Key Fields | Quality Notes |
|--------|----------|------|------------|---------------|
| | | | | |
## Hypothesis Log
<!--
WHAT: Each hypothesis you tested, the method used, and the result.
WHY: Structured tracking prevents p-hacking and makes your reasoning auditable.
EXAMPLE:
| H1: Churn > 50% for low-activity users | Chi-squared test | Confirmed (p=0.003) | High |
| H2: Feature X correlates with retention | Pearson correlation | Rejected (r=0.08) | High |
-->
| Hypothesis | Test Method | Result | Confidence |
|------------|-------------|--------|------------|
| | | | |
## Query Results
<!--
WHAT: Key queries you ran and what they revealed.
WHY: Queries are ephemeral - if you don't write down the results, they're lost on context reset.
WHEN: After EVERY significant query. Don't wait.
EXAMPLE:
### Churn rate by activity segment
Query: SELECT activity_bucket, COUNT(*), AVG(churned) FROM user_segments GROUP BY 1
Result: Low activity: 62% churn, Medium: 28%, High: 8%
Interpretation: Strong inverse relationship between activity and churn
-->
<!-- Record query, result summary, and interpretation for each significant query -->
## Statistical Findings
<!--
WHAT: Formal statistical test results with all relevant metrics.
WHY: Recording p-values, effect sizes, and confidence intervals makes results reproducible.
EXAMPLE:
| Chi-squared (churn ~ activity) | p=0.003 | Cramer's V=0.31 | Reject null: activity segments differ significantly in churn |
| Pearson (feature_x ~ retention) | p=0.42 | r=0.08 | Fail to reject: no meaningful correlation |
-->
| Test | p-value | Effect Size | Conclusion |
|------|---------|-------------|------------|
| | | | |
## Technical Decisions
<!--
WHAT: Analytical method choices with reasoning.
EXAMPLE:
| Use log transform on revenue | Right-skewed distribution, normalizes for parametric tests |
-->
| Decision | Rationale |
|----------|-----------|
| | |
## Issues Encountered
| Issue | Resolution |
|-------|------------|
| | |
## Resources
<!-- URLs, file paths, documentation links -->
-
## Visual/Browser Findings
<!--
CRITICAL: Update after viewing charts, dashboards, or browser results.
Multimodal content doesn't persist in context - capture as text immediately.
-->
-
---
*Update this file after every 2 view/browser/search operations*
*This prevents visual information from being lost*