---
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.
title: analysis-assumptions-log
canonical_url: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/analysis-assumptions-log
last_updated: 2026-09-27T17:26:11.000Z
---

> **Skill from skilld.dev.** Follow the instructions below for this session. You do not need to install anything.
>
> Supporting files, fetch one when the Skill refers to it: [assets/assumptions_log_template.md](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/analysis-assumptions-log/assets/assumptions_log_template.md), [references/assumption_categories.md](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/analysis-assumptions-log/references/assumption_categories.md), [scripts/assumptions_tracker.py](https://skilld.dev/api/skills-raw/nimrodfisher/data-analytics-skills/analysis-assumptions-log/scripts/assumptions_tracker.py).
>
> If the user asked to install this Skill, run `npx skilld install nimrodfisher/data-analytics-skills/analysis-assumptions-log`. Install writes the Skill files into the project, so every session loads them.

# 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