Analysis Assumptions Log

When to use

• Starting an analysis with significant scope, method, or data quality choices
• Preparing work for peer review or stakeholder sign-off
• Returning to an old analysis and needing to understand prior decisions
• Working in a regulated environment where auditability is required
• Handing off an analysis to another analyst

Process

1. Initialize the log — create a log entry for the analysis with its name, date, analyst, and the decision it informs. Use scripts/assumptions_tracker.py to initialise a structured JSON log.
2. Enumerate data assumptions — document representativeness, completeness, how missing values are handled, and any known quality issues. For each assumption, record the rationale and confidence level (high/medium/low). See references/assumption_categories.md for the full taxonomy.
3. Enumerate business logic assumptions — record metric definitions, time windows, inclusion/exclusion rules, and any definitions provided by stakeholders. Note alternatives considered.
4. Enumerate statistical assumptions — record distribution assumptions, independence claims, stationarity, or model assumptions relevant to the methods used.
5. Assess impact and flag critical assumptions — for each low-confidence assumption with high impact if wrong, create a validation plan. Run scripts/assumptions_tracker.py --report to surface the critical list.
6. Validate and close — as validation occurs, update the log with results. Export assets/assumptions_log_template.md for peer review sign-off before delivery.

Inputs the skill needs

• Analysis name and the decision it informs
• Data sources, time period, and population being analysed
• Key methodological choices made (and alternatives considered)
• Stakeholder-provided business rule definitions
• Any known data quality issues

Output

• scripts/assumptions_tracker.py — CLI tool to log assumptions, flag critical ones, and export a summary
• assets/assumptions_log_template.md — completed log for peer review and audit trail