What is a Synthetic Data?

Synthetic data is used when real data is scarce, expensive to label, privacy-sensitive, or doesn't cover enough edge cases. It can be generated by rule-based simulation (modeling a process and sampling outcomes), by perturbing real data (adding controlled noise or variation), or by having a generative model produce plausible new examples conditioned on patterns learned from real data. Done well, it lets teams train or evaluate models on scenarios that are rare or risky to collect naturally — fraud patterns, safety-critical edge cases, underrepresented demographic groups — without needing to wait for or expose real instances. The risk is that synthetic data inherits and can amplify the biases or blind spots of whatever generated it — if the generator never saw a pattern, or systematically misrepresents one, the synthetic data quietly encodes that gap into anything trained on it. Because of this, synthetic data usually supplements rather than fully replaces real-world data, and its statistical fidelity to the real distribution needs validation before it's trusted for downstream training or evaluation.