ab-test-setup

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

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

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.

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: