What if you're on a tight on time and want to run your A/B test faster, or you don't have a large enough sample size for statistical significance?

To accelerate A/B tests with limited data, several advanced strategies can be employed. CUPED (Controlled-experiment With Pre-Experiment Data) helps reduce variance by incorporating pre-experiment metrics, leading to faster detection of significant differences. Interleaving presents variations to users in a mixed order, allowing for more comparative data collection per session, particularly effective for ranking or recommendation systems. Multi-Armed Bandit (MAB) algorithms dynamically shift traffic towards better-performing variations in real-time, optimizing outcomes more quickly than traditional A/B tests. Finally, Bayesian A/B testing provides probabilistic results, enabling earlier decision-making as it doesn't rely on a fixed sample size for significance.