In the experimental sciences, statistical power analyses are often used
before data collection to determine the required sample size. However,
traditional power analyses can be costly when data are difficult or expensive
to collect. We propose synthetic power analyses; a framework for estimating
statistical power at various sample sizes, and empirically explore the
performance of synthetic power analysis for sample size selection in cognitive
neuroscience experiments. To this end, brain imaging data is synthesized using
an implicit generative model conditioned on observed cognitive processes.
Further, we propose a simple procedure to modify the statistical tests which
result in conservative statistics. Our empirical results suggest that synthetic
power analysis could be a low-cost alternative to pilot data collection when
the proposed experiments share cognitive processes with previously conducted
experiments.

本论文提出了一种名为 “合成功效分析” 的框架，通过在观察到的认知过程的条件下使用隐式生成模型合成脑成像数据，以实验性地探究其在样本大小选择方面的性能，结果表明，这种方法可以作为先导数据收集的低成本替代方案。