Definition
A/B testing (also called split testing) is a method of comparing two variants of a page, feature, or flow by randomly assigning users to one or the other and measuring which produces a better outcome. Users see either variant A (control) or variant B (treatment) at random, so any difference in conversion is attributable to the change you made, not to confounding traffic differences.
A good A/B test requires a clear metric (form submissions per visitor), a big enough sample size to detect the expected effect (usually thousands of visitors per variant), and a fixed analysis horizon to avoid peeking. Running many small tests without discipline produces false positives that do not survive when applied to production.
How SheetLinkWP relates to A/B Testing
SheetLink Forms does not run A/B tests itself, but the data it captures - UTMs, referrer, landing page URL, submission content - is exactly what a well-designed A/B test needs for analysis. Many customers push submission data from their Sheet into a BI tool (Metabase, Looker Studio) and slice by the test-variant URL parameter to measure conversion per variant. The AI Analytics add-on can also surface submission-rate differences between UTM groups as part of its weekly brief.