A/B Testing in Business Analytics: Designing Experiments

A/B testing helps quantify the impact of product and process changes by comparing outcomes between groups exposed to different variations. Designing a strong experiment includes defining hypotheses, selecting primary and secondary metrics, calculating sample sizes, ensuring random assignment, and setting success thresholds in advance. Teams should also consider practical issues such as novelty effects, seasonality, and data tracking reliability. Finally, results must be interpreted with statistical rigor and operational context to avoid overfitting conclusions to the observed data.