# Experiment Design

> Design robust experiments to test product hypotheses. Define metrics, sample size, and success criteria.

- Skill: `itseffi/experiment-design` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add itseffi/experiment-design`
- Raw SKILL.md: https://api.skillmd.com/api/skills/itseffi/experiment-design/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: itseffi (https://skillmd.com/u/itseffi)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/itseffi/experiment-design

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# Experiment Design

Design robust experiments to test product hypotheses.

## When to Use

When you have a hypothesis and need to design an experiment to validate it.

## The Process

### 1. Factor Breakdown

Analyze your goal and system:
- What factors could influence the outcome?
- Which are controllable vs. environmental?
- What are the key variables?

### 2. Experiment Structure

For each factor, define:
- **Hypothesis:** What you expect to happen
- **Independent variable:** What you're changing
- **Dependent variable:** What you're measuring
- **Control group:** Baseline comparison
- **Experimental group:** Who gets the change
- **Measurement method:** How you'll collect data
- **Confounding variables:** What else could affect results

### 3. Sample Design
- Who participates?
- How many needed for statistical significance?
- How will you recruit/select?

### 4. Timeline
- How long to run?
- When to check results?
- What's the minimum detectable effect?

### 5. Stop/Scale Rules
- What results mean "stop"?
- What results mean "scale"?
- What's inconclusive?

## Output Format

For each experiment:
- Hypothesis statement
- Variables (independent, dependent)
- Groups (control, experimental)
- Sample size and selection
- Measurement approach
- Success criteria
- Stop/scale rules

## When Not to Use

Do not use this skill when the request is unrelated, low-stakes, or better handled by a simpler direct response.