What is A/B testing used for?

A/B testing is a powerful technique used primarily to compare two versions of a variable to determine which version performs better. This method involves splitting a population into two groups: one group experiences the ‘A’ version and the other group experiences the ‘B’ version. By analyzing the performance metrics, such as conversion rates, user engagement, or other key performance indicators relevant to the experiment, businesses can derive insights into which version is more effective in achieving the desired outcome.

For instance, a company might want to evaluate two different designs of a webpage to see which results in more sign-ups. By conducting A/B testing, the business can obtain data-driven evidence to make informed decisions about which design to implement more widely.

The other options address different analytical methods or performance measurements that do not align with the fundamental purpose of A/B testing. For instance, analyzing cost-effectiveness involves financial assessments rather than direct comparisons of performance between two variables. Measuring prediction accuracy pertains to model evaluation in analytics, and employee performance evaluations are more qualitative assessments unrelated to the A/B testing framework. Thus, the core purpose of A/B testing remains focused on performance comparison, making it distinctly suited for option B.