/ab-test-setup

name: ab-test-setup
description: "Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness."
risk: critical
source: community
date_added: "2026-02-27"

A/B Test Setup

1️⃣ Purpose & Scope

Ensure every A/B test is **valid, rigorous, and safe** before a single line of code is written.

• Prevents "peeking"
• Enforces statistical power
• Blocks invalid hypotheses

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2️⃣ Pre-Requisites

You must have:

• A clear user problem
• Access to an analytics source
• Roughly estimated traffic volume

Hypothesis Quality Checklist

A valid hypothesis includes:

• Observation or evidence
• Single, specific change
• Directional expectation
• Defined audience
• Measurable success criteria

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3️⃣ Hypothesis Lock (Hard Gate)

Before designing variants or metrics, you MUST:

• Present the **final hypothesis**
• Specify:
• Target audience
• Primary metric
• Expected direction of effect
• Minimum Detectable Effect (MDE)

Ask explicitly:

> “Is this the final hypothesis we are committing to for this test?”

**Do NOT proceed until confirmed.**

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4️⃣ Assumptions & Validity Check (Mandatory)

Explicitly list assumptions about:

• Traffic stability
• User independence
• Metric reliability
• Randomization quality
• External factors (seasonality, campaigns, releases)

If assumptions are weak or violated:

• Warn the user
• Recommend delaying or redesigning the test

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5️⃣ Test Type Selection

Choose the simplest valid test:

• **A/B Test** – single change, two variants
• **A/B/n Test** – multiple variants, higher traffic required
• **Multivariate Test (MVT)** – interaction effects, very high traffic
• **Split URL Test** – major structural changes

Default to **A/B** unless there is a clear reason otherwise.

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6️⃣ Metrics Definition

Primary Metric (Mandatory)

• Single metric used to evaluate success
• Directly tied to the hypothesis
• Pre-defined and frozen before launch

Secondary Metrics

• Provide context
• Explain _why_ results occurred
• Must not override the primary metric

Guardrail Metrics

• Metrics that must not degrade
• Used to prevent harmful wins
• Trigger test stop if significantly negative

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7️⃣ Sample Size & Duration

Define upfront:

• Baseline rate
• MDE
• Significance level (typically 95%)
• Statistical power (typically 80%)

Estimate:

• Required sample size per variant
• Expected test duration

**Do NOT proceed without a realistic sample size estimate.**

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Tracking Verification (Required before Gate 8)

Before entering the Execution Readiness Gate below, run through this checklist to make "Tracking is verified" mean something concrete:

1. **Event firing:** Trigger each event the primary and secondary metrics depend on (sign-up, add-to-cart, custom event) on staging or a debug page, and confirm it lands in your analytics destination within 30 seconds. 2. **Variant attribution:** Verify that the variant assignment ID is attached to every fired event — not just the entry event. Use your analytics' raw event view to compare a sample of 5+ events per variant. 3. **De-duplication:** Confirm that a user reloading the page does not cause double-counted events. If your stack uses client-side de-duping, the variant ID must be part of the dedup key. 4. **Sample randomization:** Pull the first 100 assignment records from your assignment table; the variant split should be within ±5% of the configured allocation. 5. **Guardrail metric pipeline:** Each guardrail metric defined in §6️⃣ must have a working dashboard or alert by the time the test launches.

If any of the above fails, stop and resolve it before Gate 8.

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8️⃣ Execution Readiness Gate (Hard Stop)

You may proceed to implementation **only if all are true**:

• Hypothesis is locked
• Primary metric is frozen
• Sample size is calculated
• Test duration is defined
• Guardrails are set
• Tracking is verified

If any item is missing, stop and resolve it.

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Running the Test

During the Test

**DO:**

• Monitor technical health
• Document external factors

**DO NOT:**

• Stop early due to “good-looking” results
• Change variants mid-test
• Add new traffic sources
• Redefine success criteria

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Analyzing Results

Analysis Discipline

When interpreting results:

• Do NOT generalize beyond the tested population
• Do NOT claim causality beyond the tested change
• Do NOT override guardrail failures
• Separate statistical significance from business judgment

Interpretation Outcomes

| Result | Action | | -------------------- | -------------------------------------- | | Significant positive | Consider rollout | | Significant negative | Reject variant, document learning | | Inconclusive | Consider more traffic or bolder change | | Guardrail failure | Do not ship, even if primary wins |

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Documentation & Learning

Test Record (Mandatory)

Document:

• Hypothesis
• Variants
• Metrics
• Sample size vs achieved
• Results
• Decision
• Learnings
• Follow-up ideas

Store records in a shared, searchable location to avoid repeated failures.

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Refusal Conditions (Safety)

Refuse to proceed if:

• Baseline rate is unknown and cannot be estimated
• Traffic is insufficient to detect the MDE
• Primary metric is undefined
• Multiple variables are changed without proper design
• Hypothesis cannot be clearly stated

Explain why and recommend next steps.

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Key Principles (Non-Negotiable)

• One hypothesis per test
• One primary metric
• Commit before launch
• No peeking
• Learning over winning
• Statistical rigor first

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Final Reminder

A/B testing is not about proving ideas right. It is about **learning the truth with confidence**.

If you feel tempted to rush, simplify, or “just try it” — that is the signal to **slow down and re-check the design**.

When to Use

This skill is applicable