Probabilistic Reasoning Versus Falsificationism and its Statistical Variations

**G. D'Agostini
Università ``La Sapienza'' and INFN, Rome, Italy
(giulio.dagostini@roma1.infn.it,
www.roma1.infn.it/~dagos)
**

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 Poincaré'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 naïve falsificationism and conventional,
frequentistic hypothesis tests are presented,
and the alternative, probabilistic approach is
outlined.

Invited talk at the 2004 Vulcano Workshop on *Frontier Objects
in Astrophysics and Particle Physics*, Vulcano (Italy) May 24-29, 2004.

**Reference** to this paper: physics/0412148

**Printable versions and related topics** at this URL.

- Inference, forecasting and related uncertainty
- Falsificationism and its statistical variations
- Forward to the past: probabilistic reasoning
- Conclusions
- Bibliography
- About this document ...