A/B testing is a method used for the comparison of two versions of an app or webpage to determine the better performer between them. Also known as bucket testing, A/B testing occurs when two interfaces are compared by showing them (A and B) to random visitors simultaneously, and the better performer is determined by using statistical analysis. Running the test to compare a current experience against a variation allows you to determine the effect of any change made to your app or website. A/B testing removes any form of doubt in website optimization and enables you to make educated decisions. By measuring the effect of every change, you can ensure that the changes made result in positive outcomes. A/B testing allows organizations, individuals, and teams to collect data on the outcome of making meticulous alterations to the experience of their users. Therefore, they can effectively make hypotheses, and learn the impact of different elements in their experience. A/B testing is increasingly becoming a useful statistical tool in Machine Learning as many variants of this test have been created.