ab-test-setup

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# 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.**

From nicolas2601/router-skills, MIT.

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