A/B test

• Validating UX, pricing, onboarding, or messaging changes.
• Choosing between two implementation options with measurable tradeoffs.
• Reducing decision risk before broad rollout.

A/B testing reduces opinion-led decisions and helps teams invest in changes that move meaningful metrics.

Define hypothesis, primary metric, guardrails, minimum detectable effect, and stop rules before launching the test.

Variant B changes onboarding headline and checklist sequence; activation improves by 5.2 points without harming retention.

• Running tests without pre-defined decision rules.
• Peeking early and stopping on noise.
• Ignoring sample size and traffic constraints.