To understand how A/B testing works, let’s take a look at the example below.    Kush’s goal is to get more newsletter sign-ups on a website page. The current web design (in this case, to be referred to as “the controlled”) is performing decently, but he wants to test out if his new landing page (to be referred to as “the variant”) could get more leads. Instead of jumping to conclusions on  which landing page would perform better, Kush decides to run an A/B test.    First he publishes Website A (the original) and waits a week to collect results. Then he publishes Website B (the variation) and waits another week to collect results. Once both of the results are collected, their performance metrics can be compared to decide which website will gain traction better.    However, keep in mind that whenever an A/B test is performed, only one element at a time should be tested or else it becomes difficult to determine what qualifies as a successful test. For instance, it is easy to know what causes Banner A to perform better than Banner B if the only thing being tested is the ad copy. On the other hand, if the variations made include different copy, imagery, and format - it will not make a good basis for 1:1 comparison as it is almost an entirely different page altogether.