What is A/B testing?
A/B testing (alternatively designated as split testing or bucket testing) implements controlled experimentation methodologies comparing multiple application or webpage variants to determine optimal performance characteristics through quantitative analysis.
Experimental frameworks employ randomized user cohort assignment distributing traffic across variant implementations, enabling unbiased performance comparison under equivalent operational conditions.
Statistical analysis methodologies evaluate variant performance against predefined conversion objectives, identifying implementations demonstrating superior user engagement or business metric optimization.
A/B testing constitutes an outcome rather than prerequisite of continuous delivery implementation, with causality flowing from deployment capability toward experimentation practices.
Continuous delivery infrastructure enables rapid MVP deployment to production environments with minimal lead time, creating foundational capabilities supporting iterative experimentation workflows.
Feature experimentation objectives typically focus on conversion rate optimization, user engagement enhancement, and business metric improvement through data-driven iteration.
Continuous experimentation cultures leverage persistent hypothesis testing with quantitative impact measurement informing product development decisions.
While comprehensive A/B testing methodology exceeds this module's scope, its significance as a continuous delivery-enabled practice warrants foundational coverage supporting further independent exploration.