A/B Test Power Overview
An A/B test compares a control with a variant. Power tells you the chance of detecting a real effect. Low power can hide useful changes. It can also waste traffic. A strong plan sets the baseline rate, expected lift, traffic split, alpha, and target power before launch.
Why Power Matters
Power is usually planned near eighty percent or ninety percent. Higher power needs more visitors. A smaller lift also needs more visitors. This calculator estimates achieved power from current sample sizes. It also estimates required samples for a target power. Use both results before changing a button, offer, price, headline, or checkout step.
Inputs and Interpretation
Baseline rate is the current conversion rate. Variant rate is the expected or observed conversion rate. Alpha is the false positive risk. A two tailed test checks for any meaningful difference. A one tailed test checks one direction. The allocation ratio controls how traffic is split. A ratio of one means equal traffic. A ratio of two gives the variant twice as much traffic as control.
Planning Better Experiments
Start with a practical minimum detectable effect. Do not chase tiny lifts unless you have enough traffic. Enter daily visitors to estimate days needed. Keep the test running through full business cycles. Avoid stopping early because early results can swing. Check data quality, tracking rules, and audience overlap. Power math assumes independent visitors and a stable conversion process.
Using Results Safely
The required sample result is an estimate. Real experiments can face seasonality, bots, repeated users, and tracking gaps. Treat power as a planning guide, not a promise. Combine it with product knowledge and risk tolerance. A result with enough power is easier to trust. A test with weak power may still be useful for learning. It should not carry major launch decisions alone. When reporting the test, include visitors, conversions, rates, lift, confidence level, and the planned rule. This makes later reviews easier. It also keeps teams from mixing planning math with post test preference. If the business impact is large, ask a statistician to review assumptions. Good preparation reduces noisy debates and improves learning from every experiment cycle. Document decisions before launch and keep raw data archived securely too.