【摘要】 作为数据科学的核心方法论体系，统计学在人工智能时代正经历深刻的范式变革。这一变革主要体现在数据抽样方法的迭代、数据采集与处理方式的革新、降本提质的实现以及学科体系的完善四个方面。同时，人工智能的应用为统计学的发展带来了新的机遇，表现为统计建模方式的转型、统计推断方法的革新、因果分析体系的演进、数据整合机制的重构和决策支持系统的升级等，推动统计学理论与方法层面的创新突破。大数据与机器学习技术的兴起也给传统统计学带来前所未有的挑战，包括理论体系的创新、方法体系的融合、数据类型的拓展和统计软件的开发。为有效应对挑战，统计学应进一步深化与人工智能技术的跨学科融合，提升统计模型的泛化能力和可解释性，构建适应数智时代复杂数据特征的新理论与方法体系，加快推进统计学持续创新与高质量发展。更多还原

【Abstract】 As a core methodological system of data science, statistics is undergoing profound paradigm shifts in the age of artificial intelligence(AI). Such transformations are primarily reflected in four dimensions: iteration of data sampling methods, innovation in data collection and processing approaches, cost reduction and quality improvement, and refinement of the disciplinary system. Meanwhile, the application of AI has provided new opportunities for the development of statistics, specifically manifested as transitions in statistical modeling techniques, innovations in statistical inference methods, evolution of causal analysis frameworks, reconstruction of data integration mechanisms, and intelligent upgrades of decision-support systems, collectively driving breakthroughs in statistical theories and methods. However, the rise of big data and machine learning also poses unprecedented challenges to traditional statistics, including the self-adaptation of theoretical frameworks, integration of methodological systems, expansion of data types, and the development of statistical software. To effectively respond to these challenges, statistics should further deepen interdisciplinary integration with AI technologies, enhance the generalization and interpretability of statistical models, and construct novel theories and methodological systems that accommodate the complex characteristics of big data, thus promoting sustained innovation and high-quality development of statistics in the AI age.更多还原