What is A/B testing primarily used for in data analytics?

A/B testing is primarily used to compare two versions of a variable, such as two different web pages, marketing emails, or product features, to determine which one performs better in achieving specific goals, such as higher conversion rates or increased user engagement. This method involves dividing a sample into two groups, where one group is exposed to version A and the other to version B, allowing analysts to collect data on the performance of each version. The goal is to identify the version that yields better results based on predetermined metrics, making it a practical tool for optimizing decision-making in marketing and user experience.

In contrast, the other options focus on different aspects of data analytics. Auditing processes involve reviewing data quality and processes rather than comparing variations to enhance performance. Analyzing historical data trends is more about understanding past behavior rather than testing current versions of variables. Finally, cleaning data sets pertains to preparing data for analysis by removing inaccuracies or inconsistencies, which is a different function than the comparative nature of A/B testing.