# Quantitative Analysis

Answers a business question with data without fooling yourself, framing the question...

- Repository: https://github.com/cbrock84/headcount (2554 stars)
- Category: Business
- License: MIT
- Last updated: 2026-10-18
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add cbrock84/headcount --skill quantitative-analysis -g -a claude-code -y`
Codex: `npx skills add cbrock84/headcount --skill quantitative-analysis -g -a codex -y`
Cursor: `npx skills add cbrock84/headcount --skill quantitative-analysis -g -a cursor -y`
Gemini CLI: `npx skills add cbrock84/headcount --skill quantitative-analysis -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add cbrock84/headcount`

## Description

Answers a business question with data without fooling yourself, framing the question so an answer would change something, choosing the right comparison, checking the data before trusting it, recognizing the traps that produce confident wrong answers (aggregation reversals, survivorship, regression to the mean, multiple comparisons), and reporting uncertainty honestly. Use this to run an analysis, review one before acting on it, or work out why two people looking at the same data reached opposite conclusions.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Quantitative analysis

A wrong answer here is rarely an arithmetic error. It is a correct calculation on the wrong
comparison, or on data that does not mean what the field name suggests.

## Frame the question so that an answer changes something

Start from the decision. "How is retention doing" has no answer; "is the cohort we changed
onboarding for retaining better than the one before it, enough to justify rolling it out" does.

Write down what you expect to find and what you would do in each case before you look. If every
possible result leads to the same action, the analysis is not worth running, and knowing that in
advance is worth more than the analysis would have been.

## Choose the comparison before the metric

Almost every meaningful number is a comparison, and the choice of what to compare against does more
work than the calculation.

- **Against what it was**, needs a period long enough to see through seasonality and noise.
- **Against what it would have been**, the strongest comparison and the hardest to construct. A
  holdout group, a matched segment, a pre-trend extended forward.
- **Against a peer or a benchmark**, only useful if the definitions genuinely match, which they
  usually do not.

**Name the counterfactual explicitly.** "Revenue rose after the campaign" is a comparison against
nothing, and it is the single most common way credit is claimed for a trend that was already
happening.

## Interrogate the data before you trust it

Look at the raw rows. Check when collection started and whether the definition changed partway.
Check null rates, duplicates, and test or internal accounts still in the set. Check whether recent
periods are still filling in, partial data at the tail is what produces the "sudden decline" that
resolves itself a week later.

**A field's name is not its definition.** Find out what actually writes it and under what
conditions, especially for anything named status, type, active, or created.

The rest: https://github.com/cbrock84/headcount/blob/HEAD/plugins/data-analytics/skills/quantitative-analysis/SKILL.md