Venue: Meeting room 5-6 Biomedicum 1 Meilahti
Timing: 15:30 onwards
Presenter: Tuomas Puoliväli
MultiPy: Multiple hypothesis testing in Python
The reproducibility of research findings has been recently questioned in many fields of science. This problem is partially caused by testing multiple hypotheses simultaneously, which increases the number of false positive findings if the corresponding p-values are not properly corrected. While this multiple testing problem is well known and has been extensively studied for decades, the classic and advanced methods are yet to be implemented into an accessible and comprehensive Python package. Here we developed a software called multipy (MULTIple hypothesis in PYthon), which is an open-source, unit-tested, and freely available collection of procedures for controlling the family-wise error rate and the false discovery rate. We hope that this effort will help to improve current data analysis practices, as well as facilitate building software for large-scale group analyses.