AI-Enhanced Early Stopping Decision Framework for A/B Testing: A Machine Learning Approach to Optimize Experimental Efficiency

Yi Wang

Journal of Sustainability, Policy, and Practice, 2025, vol. 1, issue 2, 86-97

Abstract: Traditional A/B testing frameworks suffer from inefficiencies in duration management, leading to resource waste and delayed decision-making. This paper presents an AI-enhanced early stopping decision framework that leverages machine learning algorithms to optimize experimental efficiency. Our framework incorporates dynamic threshold adjustment mechanisms and predictive stopping models to reduce testing duration while maintaining statistical rigor. The proposed approach integrates sequential analysis with machine learning techniques, enabling real-time decision-making based on accumulating evidence. Experimental evaluation demonstrates significant improvements in testing efficiency, with average duration reductions of 35% compared to traditional fixed-duration approaches. The framework maintains statistical power while providing robust stopping criteria that adapt to varying experimental conditions. Implementation results across multiple domains validate the practical applicability and scalability of the proposed methodology.

Keywords: A/B testing; early stopping; machine learning; experimental design (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dba:jsppaa:v:1:y:2025:i:2:p:86-97

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