  The machine learning community adopted the use of null hypothesis
significance testing (NHST) in order to ensure the statistical validity of
results. Many scientific fields however realized the shortcomings of
frequentist reasoning and in the most radical cases even banned its use in
publications. We should do the same: just as we have embraced the Bayesian
paradigm in the development of new machine learning methods, so we should also
use it in the analysis of our own results. We argue for abandonment of NHST by
exposing its fallacies and, more importantly, offer better - more sound and
useful - alternatives for it.
