  A lot of Machine Learning (ML) and Deep Learning (DL) research is of an
empirical nature. Nevertheless, statistical significance testing (SST) is still
not widely used. This endangers true progress, as seeming improvements over a
baseline might be statistical flukes, leading follow-up research astray while
wasting human and computational resources. Here, we provide an easy-to-use
package containing different significance tests and utility functions
specifically tailored towards research needs and usability.
