ab-testing

name	ab-testing
description	Use when turning a marketing, growth, CRO, pricing, onboarding, email, ad, or acquisition idea into a useful experiment or test plan.

A/B Testing

Turn a growth idea into a test that can actually change a decision.

Frame The Decision

Start with the decision the experiment should inform:

• Ship, kill, iterate, scale, or investigate.
• Audience or surface being tested.
• Current baseline.
• Primary metric and guardrail metric.
• Minimum effect that would matter.
• Sample size or traffic reality.
• Time window and implementation cost.

If the traffic is too low for an A/B test, recommend a qualitative, sequential, or directional test instead.

Write The Hypothesis

Use this shape:

Because [observed problem], changing [specific thing] for [audience] should improve [primary metric] without hurting [guardrail], shown by [measurement].

Make the variant isolate one main idea. Do not mix headline, price, layout, offer, and audience changes unless the test is explicitly a bundled concept test.

Choose The Test Type

Pick the method based on traffic, risk, and decision cost:

• A/B test: enough traffic and a reversible surface.
• Before/after read: operational change where randomization is impractical.
• Concierge test: validate demand or workflow manually before building.
• Smoke test: test interest before full fulfillment.
• Fake-door test: measure intent when the feature or offer is not ready, with ethical disclosure.
• Qualitative read: use interviews, session reviews, or sales calls when numbers will be too thin.

Add decision economics:

• Cost of shipping the wrong thing.
• Cost of waiting.
• Minimum useful evidence.

Design The Test

Define:

• Control and variant.
• Inclusion and exclusion rules.
• Primary metric.
• Guardrails.
• Instrumentation requirements.
• Decision threshold.
• Stop conditions.
• Rollback plan.

Interpret Carefully

• Do not call a winner before the decision threshold is met.
• Do not ignore novelty effects.
• Segment after the primary read, not until a desired story appears.
• Treat inconclusive results as useful when they eliminate bad ideas.

Output

Experiment brief:
Decision:
Hypothesis:
Audience:
Surface:

Evidence shape:
[A/B / before-after / concierge / smoke / fake-door / qualitative]

Decision economics:
- Cost of wrong ship:
- Cost of waiting:
- Minimum useful evidence:

Control or baseline:
Variant or intervention:

Metrics:
Primary:
Guardrails:
Instrumentation:

Readiness:
- Traffic:
- Baseline:
- Minimum useful lift:
- Runtime:

Decision rules:
- Ship if:
- Iterate if:
- Kill if:

Risks:
- [risk] -> [mitigation]