Artificially generated datasets that mimic real distributions—useful for tests, demos, and privacy when handled carefully.
Synthetic data can fill gaps when production data is restricted. Risks include unrealistic distributions and leakage if derived improperly from sensitive sources.
Engineering uses
Seeding integration tests, load tests, and sandboxes—often alongside property-based testing or fuzzing workflows.