论文标题
私人假设测试社会科学
Private Hypothesis Testing for Social Sciences
论文作者
论文摘要
在进行任何实验时，我们通常必须考虑统计能力以确保有效的研究。统计能力或力量可确保我们可以观察到这种真实效果的可能性很高。但是，一些研究缺乏确定最佳样本量以确保足够功能的适当计划。因此，仔细的计划确保即使在高测量错误下，在保持类型1误差的同时也要限制了功率。我们研究了差异隐私对实验的影响，并理论上分析了由于高斯机制所需的样本量变化。此外，我们提供了一种经验方法，可以通过简单的引导来提高私人统计数据的准确性。
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.
