Synthetic examples can be created through simulation, rules or generative models. Their usefulness depends on how well they represent the conditions and relationships relevant to the intended learning or evaluation task.
Generated data can carry bias, errors or unrealistic assumptions. It should not be treated as independent real-world evidence, and evaluation needs to check whether performance transfers to appropriate observed examples.
ELI5
Synthetic data is made to provide examples instead of being collected directly from the situation being studied. It can help a system practice under controlled conditions.
For example, a program could generate tables with known patterns to train a predictor. The predictor would still need tests on suitable real tables, since success on generated patterns may not cover the complexity of actual data.
Is synthetic data always realistic?
No. Its realism depends on the generation method and assumptions, which need validation for the intended use.
Does synthetic data replace all observed evidence?
No. Real-world evaluation is often needed to assess whether the learned behavior transfers appropriately.
