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analysis-assumptions-log

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更新日2026年5月31日 05:25

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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ryudkiss-hue

ryudkiss-hue/nyc_data

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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

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.
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.
Enumerate business logic assumptions — record metric definitions, time windows, inclusion/exclusion rules, and any definitions provided by stakeholders. Note alternatives considered.
Enumerate statistical assumptions — record distribution assumptions, independence claims, stationarity, or model assumptions relevant to the methods used.
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.
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

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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

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.

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.

Enumerate business logic assumptions — record metric definitions, time windows, inclusion/exclusion rules, and any definitions provided by stakeholders. Note alternatives considered.

Enumerate statistical assumptions — record distribution assumptions, independence claims, stationarity, or model assumptions relevant to the methods used.

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.

Validate and close — as validation occurs, update the log with results. Export assets/assumptions_log_template.md for peer review sign-off before delivery.

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.