What is A/B Testing?
A/B testing (split testing) shows different versions of a feature to different user groups simultaneously and measures which performs better. It replaces gut-feel product decisions with data.
In plain English
A/B testing is like a restaurant testing two different menu descriptions for the same dish. Half the tables get version A, half get version B, and the kitchen tracks which wording leads to more orders. The winner goes on the permanent menu.
How it works
Users are randomly assigned to a control group (A) or a variant group (B) when they visit your app. Each group sees a different version of the feature being tested. After enough users see each variant, you run statistical analysis to determine if the difference in outcomes is significant or due to chance.
Why it matters for AI-built apps
AI can generate multiple UI variants or copy options quickly, but it can't predict which will resonate with your specific users. A/B testing turns that uncertainty into a structured experiment. Even small improvements to conversion rate or activation compound significantly over time.
Best practices
Test one variable at a time so you know what caused the outcome. Run tests long enough to reach statistical significance — at least 100 conversions per variant as a rough baseline. Define your success metric before starting the experiment to avoid post-hoc rationalization.
Frequently asked questions
How long should I run an A/B test?
Until you reach statistical significance, typically at least two full business cycles (weeks) to account for day-of-week variation. Use a sample size calculator before starting.
Can I A/B test with feature flags?
Yes — tools like Growthbook combine feature flags with experiment tracking. This is the most practical setup for engineering teams.
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