Learning a Size-Weight Frontier for Synthetic-Augmented Inference
How much fake data can you safely mix with real data?
When researchers use synthetic data to fill gaps in real observations, they risk getting wrong answers if they're not careful about how much synthetic data to use. This paper presents a method that identifies exactly how many synthetic samples can be safely mixed with real data—and at what weight—while still producing reliable results. In tests combining AI-generated responses with real survey data, the approach maintained accuracy while shrinking confidence intervals by substantial margins.
Synthetic data is cheap and fast to generate, but mistakes in using it waste time and money on flawed conclusions. This framework lets practitioners know precisely when they can trust their results, making it practical to combine real and synthetic data without guessing about reliability. For surveys, medical studies, and other research constrained by small sample sizes, this could make the difference between usable insights and misleading ones.