<P> In marketing and web analytics, A / B testing (bucket tests or split - run testing) is a controlled experiment with two variants, A and B. It is a form of statistical hypothesis testing or "two - sample hypothesis testing" as used in the field of statistics . In online settings, such as web design (especially user experience design), the goal of A / B testing is to identify changes to web pages that increase or maximize an outcome of interest (e.g., click - through rate for a banner advertisement). Formally the current web page is associated with the null hypothesis . A / B testing is a way to compare two versions of a single variable typically by testing a subject's response to variable A against variable B, and determining which of the two variables is more effective . </P> <P> As the name implies, two versions (A and B) are compared, which are identical except for one variation that might affect a user's behavior . Version A might be the currently used version (control), while version B is modified in some respect (treatment). For instance, on an e-commerce website the purchase funnel is typically a good candidate for A / B testing, as even marginal improvements in drop - off rates can represent a significant gain in sales . Significant improvements can sometimes be seen through testing elements like copy text, layouts, images and colors, but not always . </P>

What variable do you test in an experiment