A/B Testing Sample Size (Annual) Calculator FAQ
Answers to common questions about A/B testing sample size, conversion lift, annual traffic, confidence, power, and test duration.
Use these answers to understand what the annual A/B testing sample size calculator estimates, which inputs matter most, and where practical test results can differ from a planning model.
General A/B Testing Sample Size Questions
Core concepts behind visitor requirements for conversion experiments.
What is A/B testing sample size?
It is the estimated number of eligible visitors needed to compare a control and a variant while targeting a chosen conversion-rate difference, confidence level, and power.
What does this annual calculator estimate?
It estimates total visitors, visitors per group, expected baseline conversions, the share of annual traffic required, and approximate months needed at the entered annual traffic level.
Does a sample size represent unique users or sessions?
It should represent the experiment unit used consistently in analysis. For many website tests that is eligible visitors, but the appropriate unit depends on the experiment design.
Why is an equal traffic split commonly used?
For two groups with the same conditions, an equal split generally obtains the most statistical information from a fixed total number of visitors.
Inputs and Effect Size
How baseline conversion rate and target lift affect the calculation.
What is the baseline conversion rate?
It is the current estimated conversion rate for the control experience among visitors eligible for the experiment.
What is minimum detectable lift?
It is the smallest relative improvement the test is designed to detect. It should reflect a change that would be meaningful for the use case.
What is the difference between relative lift and percentage points?
Relative lift compares the change with the baseline. Moving from 5% to 5.5% is a 10% relative lift and a 0.5 percentage-point increase.
Why do low baseline conversion rates require more traffic?
For the same relative lift, a lower baseline produces a smaller absolute conversion-rate difference, which is harder to detect reliably.
Can I use the calculator for a negative change?
The calculator is designed around a planned positive relative lift. A two-sided test can detect differences in either direction, but the input represents the size of the change to plan around.
Confidence, Power, and Duration
How statistical settings influence required visitors and calendar time.
What confidence z-score should I enter?
A z-score of 1.96 is commonly used for approximately 95% two-sided confidence, while 2.58 is commonly used for approximately 99% two-sided confidence.
What power z-score should I enter?
A z-score of 0.84 is commonly associated with about 80% power and 1.28 with about 90% power.
Does higher power increase the sample size?
Yes. Higher power requires more evidence to improve the chance of detecting the planned effect when it exists, so the estimated sample rises.
How is test duration calculated from annual traffic?
The calculator divides the total required sample by annual eligible visitors and multiplies by 12 months.
What does more than 100% annual traffic required mean?
It means the estimated sample exceeds the number of eligible visitors expected in one year, assuming the stated traffic level.
Accuracy and Practical Use
Important boundaries around interpreting a planning estimate.
Is the calculator result a guarantee that an A/B test will be significant?
No. It is a planning estimate. Random variation and differences between assumptions and live data can affect the observed result.
Does the calculator account for seasonality?
No. The duration estimate assumes traffic arrives evenly and behavior is stable, so seasonal changes can alter actual timing and results.
Does it account for multiple variants?
No. Testing more than one variant usually requires additional planning because traffic is split across more groups and multiple-comparison considerations may apply.
Can I stop the test once a result looks significant?
Early stopping can make results less reliable unless the testing method was designed to handle interim checks. The calculator assumes a planned sample target.
Should I use all site visitors as annual traffic?
Use only visitors who are actually eligible to see and participate in the experiment, not necessarily all site traffic.
What is sample size in A/B testing?
It is the number of eligible visitors needed to compare control and variant at selected confidence and power settings.
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