  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.
