Synthetic data generation, a cornerstone of Generative Artificial Intelligence (GAI), 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. This article introduces the Synthetic Data Generation for Analytics (Syn) framework. This framework employs statistical methods on high-fidelity synthetic data generated by advanced models such as tabular diffusion and Generative Pre-trained Transformer (GPT) models. These models, trained on raw data, are further enhanced with insights from pertinent studies through knowledge transfer. A significant discovery within this framework is the generational effect: the error of a statistical method on synthetic data initially diminishes with additional 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 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（GAI）的基石，标志着数据科学的范式转变：它在解决数据稀缺性与隐私问题的同时，实现了前所未有的性能表现。随着合成数据日益受到重视，一个问题随之浮现：相较于原始数据，统计方法应用于合成数据时的准确性如何？本文提出用于分析的合成数据生成框架（Syn框架）。该框架对由高级模型（如表列扩散模型与生成式预训练Transformer（GPT）模型）生成的高保真合成数据应用统计方法。这些模型基于原始数据训练，并通过知识迁移吸收相关研究的洞见得以增强。该框架中的一个重要发现是"代际效应"：统计方法在合成数据上的误差最初会随合成数据量增加而减小，但随后可能增大或趋于平稳。这一现象根植于原始数据分布复现的复杂性，揭示了"反射点"——即由特定误差指标确定的合成数据最优规模阈值。通过三个案例研究——文本情感分析、结构化数据预测建模以及表列数据推断——我们证明了该框架相较于传统方法的有效性。我们强调其能够增强包括梯度提升预测和假设检验在内的多种统计方法，从而凸显合成数据生成在数据科学中的变革潜力。