# Data Validation Magnitude Checks

> Sub-skill of data-validation: Magnitude Checks (+2).

- Skill: `vamseeachanta/data-validation-magnitude-checks` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/data-validation-magnitude-checks`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/data-validation-magnitude-checks/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/data-validation-magnitude-checks

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# Magnitude Checks (+2)

## Magnitude Checks


For any key number in your analysis, verify it passes the "smell test":

| Metric Type | Sanity Check |
|---|---|
| User counts | Does this match known MAU/DAU figures? |
| Revenue | Is this in the right order of magnitude vs. known ARR? |
| Conversion rates | Is this between 0% and 100%? Does it match dashboard figures? |
| Growth rates | Is 50%+ MoM growth realistic, or is there a data issue? |
| Averages | Is the average reasonable given what you know about the distribution? |
| Percentages | Do segment percentages sum to ~100%? |


## Cross-Validation Techniques


1. **Calculate the same metric two different ways** and verify they match
2. **Spot-check individual records** -- pick a few specific entities and trace their data manually
3. **Compare to known benchmarks** -- match against published dashboards, finance reports, or prior analyses
4. **Reverse engineer** -- if total revenue is X, does per-user revenue times user count approximately equal X?
5. **Boundary checks** -- what happens when you filter to a single day, a single user, or a single category? Are those micro-results sensible?


## Red Flags That Warrant Investigation


- Any metric that changed by more than 50% period-over-period without an obvious cause
- Counts or sums that are exact round numbers (suggests a filter or default value issue)
- Rates exactly at 0% or 100% (may indicate incomplete data)
- Results that perfectly confirm the hypothesis (reality is usually messier)
- Identical values across time periods or segments (suggests the query is ignoring a dimension)