A/B tests are known as one of the most important tests used in technical fields. Specifically in areas such as user experience design or e-marketing.
These tests simply allow you to compare and contrast two options. Usually those options are different versions of the same thing. Such as comparing two suggested book cover designs, or designing a product.
A/B tests are known by other names, such as split tests, or pot tests. The last name is derived from the experience of putting the same thing in two different containers.
A/B testing helps greatly in reducing confusion when making decisions. These tests allow you to test different hypotheses and gather data first before making any change or decision you have in mind.
When using these tests in large design or development projects, or even marketing an important product with a large budget, these tests can greatly facilitate the work, and save a lot of money.
The concept of A/B testing was first introduced by statistician and biologist Ronald Fisher in 1920. This concept was first used by the same scientist in an agricultural project.
Currently, this concept is used in a large number of fields, most notably web design and marketing.
For example, in the case of designing a new website, the design team chooses between more than one possible form of the website. However, they need a way to determine the layout that will lead visitors to spend more time on the site.
These tests are implemented by creating a fixed model for the design of the site, and it is called the standard or control model. In addition to another model to which the required changes are applied, the Variant model. Of course, the first form is A and the second is B.
During the testing phases, the standard and variable models are shown to a random group of people. Their opinions are collected and their reactions tracked, to gauge how well the changes to the original model are.
The concept of A/B tests is based on the randomness of the sample. For example, if Facebook wants to test a new design change to the app or website, it implements the change and presents it to half of the subscribers, while installing the standard original design for the other half.
Accordingly, the extent to which users interact with the new change is tracked, and compared to the original design. A/B testing is done over a relatively long time span, and free tools are available to calculate the appropriate time span.