analysis-assumptions-log

name	analysis-assumptions-log
description	Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.

When to use

• Starting any significant analysis where assumptions will influence conclusions
• Preparing work for peer review or stakeholder presentation
• Returning to an analysis after a break and needing to reconstruct decisions
• In regulated or high-stakes environments requiring audit trails
• Handing off an analysis to another analyst

Process

1. Initialise the log — create a structured JSON entry via scripts/assumptions_tracker.py with analysis name, author, date, and data sources
2. Document data assumptions — for each data source: record what population is included/excluded, how nulls are handled, what time period is used, and confidence level (high / medium / low)
3. Document business logic assumptions — record how business terms are defined, which calculation approach was chosen and why, and what alternatives were considered
4. Document statistical assumptions — note any statistical assumptions relevant to the methods used (e.g. normality for t-tests, independence of observations)
5. Assess impact — for each assumption, estimate impact if wrong (high / medium / low); flag high-impact assumptions for validation before finalising conclusions

Inputs the skill needs

• Required: analysis name and data sources being used
• Required: methodological choices made so far
• Optional: stakeholder-provided definitions for business terms
• Optional: known data quality issues

Output

• scripts/assumptions_tracker.py — CLI tool for logging assumptions and flagging critical ones
• assets/assumptions_log_template.md (filled) — structured log for peer review and audit trail