A data analysis pipeline is a structured sequence of processing steps that transforms raw data into meaningful insights by effectively integrating various analysis algorithms. In this paper, we propose a novel statistical test designed to assess the statistical significance of data analysis pipelines. Our approach allows for the systematic development of valid statistical tests applicable to any data analysis pipeline configuration composed of a set of data analysis components. We have developed this framework by adapting selective inference, which has gained recent attention as a new statistical inference technique for data-driven hypotheses. The proposed statistical test is theoretically designed to control the type I error at the desired significance level in finite samples. As examples, we consider a class of pipelines composed of three missing value imputation algorithms, three outlier detection algorithms, and three feature selection algorithms. We confirm the validity of our statistical test through experiments with both synthetic and real data for this class of data analysis pipelines. Additionally, we present an implementation framework that facilitates testing across any configuration of data analysis pipelines in this class without extra implementation costs.

我们提出了一种新的统计测试方法，用于评估数据分析流水线的统计显著性，该测试方法基于选择性推断，适用于由一组数据分析组件组成的任何数据分析流水线配置。我们通过合成和真实数据的实验证实了统计测试的有效性，并提出了一个实现框架，可以在此类数据分析流水线的任何配置上进行测试，无需额外的实现成本。

通过选择性推断进行数据分析管道的统计检验