experiment-design

Design structured experiments with falsifiable hypotheses and measurable outcomes.

3|Updated Nov 19, 2025

Search Tags:#metrics #experiment design #product development #hypothesis testing #a/b testing #rollout strategy

Ready to run

npx skills add https://github.com/meaganewaller/.dotfiles --skill experiment-design-meaganewaller

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: experiment-design
Source: https://github.com/meaganewaller/.dotfiles/tree/main/home/.claude/skills/common/experiment-design
Command: npx skills add https://github.com/meaganewaller/.dotfiles --skill experiment-design-meaganewaller

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users systematically plan experiments, ensuring that ideas are tested rigorously with clear success and failure criteria, preventing wasted effort and ambiguous results.

Core Features & Use Cases

Hypothesis Formulation: Guides the creation of specific, falsifiable hypotheses using a clear formula.
Variable Identification: Helps distinguish between independent, dependent, and control variables.
Metric Selection: Assists in choosing appropriate primary, secondary, and guardrail metrics.
Rollout & Rollback Planning: Outlines strategies for phased rollouts and defines clear rollback triggers and mechanisms.
Use Case: Before launching a new feature, use this Skill to define a hypothesis like "If we change the button color to blue, then conversion rate will increase by 5% within 2 weeks, because blue is a more trustworthy color."

Quick Start

Use the experiment-design skill to plan an A/B test for a new signup flow.

Frequently Asked Questions about experiment-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design an A/B test with clear success and failure criteria?▼

Design A/B tests by formulating falsifiable hypotheses, identifying independent and dependent variables, selecting primary and guardrail metrics, and defining explicit success or failure thresholds to prevent ambiguous results.

What is a falsifiable hypothesis in product experiment design?▼

A falsifiable hypothesis in product experiment design predicts a specific measurable outcome, such as a 5% conversion increase within two weeks, which can be definitively proven false if the expected impact does not occur.

How do I plan a rollout and rollback strategy for a new feature launch?▼

Plan feature rollouts using phased deployment strategies and define explicit rollback triggers based on guardrail metrics to ensure immediate reversion if performance degrades beyond acceptable thresholds.

What metrics should I track for feature rollout experiments?▼

Track primary metrics to measure the main hypothesis impact, secondary metrics for broader behavioral changes, and guardrail metrics to monitor and prevent unintended negative effects on performance.

When should I use structured experiment design instead of a simple feature launch?▼

Use structured experiment design when you need measurable outcomes for A/B tests or performance changes, ensuring ideas are rigorously tested with clear variables and success criteria to avoid wasted effort.

How do I identify variables for an A/B test?▼

Identify A/B test variables by isolating the independent variable you are changing, the dependent variable you are measuring, and holding all other control variables constant to ensure test validity.