Inference Using Synthetic Data: Balancing Privacy, Bias, and Variance in Modern Statistical Practice

As privacy concerns grow, synthetic data have emerged as a promising solution, yet ensuring proper inference remains a critical challenge. This roundtable session explored best practices for making valid inferences from synthetic datasets, with a focus on mitigating bias and accurately estimating variance (the uncertainty of results derived from synthetic data). Participants from diverse research areas shared insights, identified gaps in current practice, and discussed challenges related to the use and dissemination of synthetic data across different fields. The goal was to foster a shared understanding of these issues and identify key areas for further development in methods and tools as this field continues to evolve.