A/B Testing: How to Run Experiments That Actually Matter
A/B testing compares two versions to see which performs better. Done right — clear hypothesis, enough sample, one change at a time — it turns guesses into evidence.
Quick version: A/B testing turns opinions into evidence — with a clear hypothesis, enough sample, and one change at a time.
The rules that make it work
- Hypothesis first — what do you expect to change, and why?
- One primary metric — decide how you'll judge success up front.
- Enough sample — too few users and the result is noise.
- Isolate the change — test one variable so you know the cause.
Experimentation is the discipline behind credible personalization and growth work — without it, you're guessing.
Frequently asked questions
What is A/B testing?
A/B testing is an experiment that shows two versions (A and B) to different users and measures which performs better on a chosen metric, so decisions are based on evidence rather than opinion.
What makes an A/B test reliable?
A clear hypothesis and primary metric, a large enough sample to reach significance, testing one change at a time, and running long enough to avoid being misled by short-term noise.
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