Year: 2025
Type: article
Abstract: A/B testing has emerged as a cornerstone methodology in data-driven business decision-making, enabling organizations to make evidence-based improvements to products, services, and digital experiences. This technical review examines the fundamental principles and best practices of A/B testing within business contexts. From establishing robust hypothesis formulation to implementing proper randomization techniques, the document explores how organizations can design experiments that deliver statistically valid insights and meaningful business impact. The review addresses critical challenges in experimentation implementation, including metric selection, sample size determination, controlling for external factors, and avoiding common statistical pitfalls such as p-hacking and Simpson's paradox. Advanced considerations including multi-variate testing, continuous experimentation culture development, and emerging methodologies like multi-armed bandit algorithms are also discussed. By mastering these technical aspects, data professionals and business stakeholders can transform A/B testing from a tactical tool into a strategic capability that consistently generates business value through empirically validated decisions, despite the reality that many online features produce no measurable improvement when subjected to rigorous experimental evaluation.
Author: Bhavana Reddy Chadagonda
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4411310068