  Testing hypotheses is an issue of primary importance in the scientific
research, as well as in many other human activities. Much clarification about
it can be achieved if the process of learning from data is framed in a
stochastic model of causes and effects. Formulated with Poincare's words, the
"essential problem of the experimental method" becomes then solving a "problem
in the probability of causes", i.e. ranking the several hypotheses, that might
be responsible for the observations, in credibility. This probabilistic
approach to the problem (nowadays known as the Bayesian approach) differs from
the standard (i.e. frequentistic) statistical methods of hypothesis tests. The
latter methods might be seen as practical attempts of implementing the ideal of
falsificationism, that can itself be viewed as an extension of the proof by
contradiction of the classical logic to the experimental method. Some
criticisms concerning conceptual as well as practical aspects of na\"\i ve
falsificationism and conventional, frequentistic hypothesis tests are
presented, and the alternative, probabilistic approach is outlined.
