A/B testing, also referred to as Split testing, is a method in statistics and marketing where two versions of a webpage, app, email, or other product are compared to determine which version performs better or yields better results.
The basic approach in A/B testing involves dividing a group of users into two equally sized subgroups: one group sees the original version (A), while the other group sees the modified version (B), which might have changes in design, content, layout, or other aspects.
By tracking user behavior, interactions, and conversions in both groups, statistical analyses can be conducted to determine which version leads to the desired outcomes. This could mean that one version generates more clicks, higher sales numbers, longer time spent on a webpage, or other measurable advantages.
A/B tests are valuable for making data-driven decisions and continuously improving products or services. They allow hypotheses to be tested and provide insights into how different changes impact user behavior. It's important in A/B testing to ensure that test groups are selected randomly and that testing conditions are as controlled as possible to obtain accurate and meaningful results.
Gitlab
