Private Hypothesis Testing for Social Sciences
Abstract
While running any experiment, we often have to consider the statistical power to ensure an effective study. Statistical power or power ensures that we can observe an effect with high probability if such a true effect exists. However, several studies lack the appropriate planning for determining the optimal sample size to ensure adequate power. Thus, careful planning ensures that the power remains high even under high measurement errors while keeping the type 1 error constrained. We study the impact of differential privacy on experiments and theoretically analyze the change in sample size required due to the Gaussian mechanisms. Further, we provide an empirical method to improve the accuracy of private statistics with simple bootstrapping.
Cite
@article{arxiv.2205.05522,
title = {Private Hypothesis Testing for Social Sciences},
author = {Ajinkya K Mulay and Sean Lane and Erin Hennes},
journal= {arXiv preprint arXiv:2205.05522},
year = {2023}
}
Comments
There are no significant alterations from version 1. We mistakenly included metadata from a journal Latex template while using its document class and it's now removed to avoid any confusion. This article has been accepted at the Workshop on Theory and Practice of Differential Privacy at ICML 2022