In statistical modeling the equivalent of synthetic data is heavily used, but not for training the actual model on. Building a generative model allows you to s

In statistical modeling the equivalent of synthetic data is heavily used, but not for training the actual model on. Building a generative model allows you to sample observations from a known distribution and confirm that your approach to learn is even able to recover the correct parameters. It's used as a basic sanity checking tool. Synthetic data is great for this purpose. Before setting out on a complex and expensive modeling task, at least verify that in the best case (i.e. your model is literally the data generating process) you could even learn the specific problem. It's surprisingly easy to define models that are fundamentally not learnable. However the idea that you could learn from synthetic data doesn't make sense, as you could just literally use the model that generates the data in the first place. If you're just learning known parameters, why waste the time training when you already have the answer? Using real data that has been heavily anonymized and stripped of PII (perturbing dates, hashing strings etc) makes some sense, assuming that PII contains no information (which is unlikely). But the more clever you're trying to be with synthetically generating features the more your are essentially just manually building a model of how the world works with obviates the need to learn a model anyway.