Synthetic data generation is the process of creating artificial data that mimics real-world data. This technique is often used in fields like machine learning and artificial intelligence to train models without needing access to sensitive or proprietary information. By generating data that reflects the characteristics of actual datasets, researchers can test algorithms and validate their performance. One common method for generating synthetic data involves using algorithms such as Generative Adversarial Networks (GANs). These algorithms learn patterns from real data and produce new data points that maintain similar statistical properties. This approach helps in overcoming privacy concerns and enhances the robustness of models by providing diverse training examples.