A/B Testing Workflow Diagram
The experiment lifecycle from hypothesis to traffic split to statistical decision.
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An A/B testing workflow diagram outlines the lifecycle of a controlled experiment, from forming a hypothesis to shipping a winning variant. Its key steps include defining a hypothesis and success metric, building control and variant experiences, splitting traffic, collecting data through an analytics pipeline, checking statistical significance, and deciding whether to roll out, iterate, or roll back.
Growth teams, PMs, and experimentation engineers use an A/B testing workflow to make evidence-based product decisions instead of shipping on gut feel. It is the backbone of conversion optimization programs and feature launches, keeping experiments rigorous and results trustworthy from one run to the next.
Great for
- Conversion optimization
- Feature launch validation
- Growth experiments
- Pricing tests
- Experimentation onboarding
Frequently asked questions
What is an A/B testing workflow?+
It is the repeatable process for running a controlled experiment: form a hypothesis, build a control and variant, split traffic between them, measure results, and decide based on statistical significance.
What are the steps of an A/B test?+
The steps are defining a hypothesis and success metric, creating the control and variant, randomly splitting traffic, collecting data, checking for statistical significance, and shipping or rolling back the change.
What is statistical significance in A/B testing?+
It is the confidence that an observed difference between variants is real and not due to random chance, commonly judged with a p-value below 0.05 or a confidence interval that excludes zero.
How long should an A/B test run?+
Run a test until it reaches a pre-calculated sample size and covers full business cycles, typically at least one to two weeks, to avoid stopping early on noisy results.
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