Synthetic data generation, a cornerstone of Generative Artificial Intelligence, signifies a paradigm shift in data science by addressing data scarcity and privacy while enabling unprecedented performance. As synthetic data gains prominence, questions arise concerning the accuracy of statistical methods when applied to synthetic data compared to raw data. In this article, we introduce the Synthetic Data Generation for Analytics framework. This framework employs statistical methods on high-fidelity synthetic data generated by advanced models such as tabular diffusion and Generative Pre-trained Transformer models. These models, trained on raw data, are further enhanced with insights from pertinent studies. A significant discovery within this framework is the generational effect: the error of a statistical method on synthetic data initially diminishes with added synthetic data but may eventually increase or plateau. This phenomenon, rooted in the complexities of replicating raw data distributions, highlights a "reflection point"--an optimal threshold in the size of synthetic data determined by specific error metrics. Through three illustrative case studies-sentiment analysis of texts, predictive modeling of structured data, and inference in tabular data--we demonstrate the effectiveness of this framework over traditional ones. We underline its potential to amplify various statistical methods, including gradient boosting for prediction and hypothesis testing, thereby underscoring the transformative potential of synthetic data generation in data science.

翻译：合成数据生成作为生成式人工智能的基石，通过解决数据稀缺性与隐私保护问题，同时实现前所未有的性能表现，标志着数据科学领域范式转变。随着合成数据日益受到重视，学界开始关注：相较于原始数据，统计方法应用于合成数据时的准确性会呈现何种差异？本文提出面向分析任务的合成数据生成框架。该框架采用基于先进模型生成的高保真合成数据实施统计方法，这些模型包括表格扩散模型与生成式预训练Transformer模型。经过原始数据训练的模型进一步融入相关研究的见解而得到增强。该框架中的一项重要发现是代际效应：统计方法在合成数据上的误差起初随合成数据量增加而减小，但可能最终呈现增大或停滞趋势。这一现象根植于复制原始数据分布的复杂性，揭示了由特定误差指标决定的合成数据规模最优阈值——"反射点"。通过三个典型案例研究——文本情感分析、结构化数据预测建模及表格数据推断——我们验证了该框架相较传统方法的有效性。研究结果凸显了该框架赋能梯度提升预测与假设检验等多样化统计方法的潜力，从而彰显合成数据生成在数据科学领域的变革性价值。