Abstract
The Fisher and Neyman-Pearson approaches to testing
statistical hypotheses are compared with respect to their
attitudes to the interpretation of the outcome, to power,
to conditioning, and to the use of fixed significance
levels. It is argued that despite basic philosophical
differences, in their main practical aspects the two
theories are complementary rather than contradictory and
that a unified approach is possible that combines the best
features of both. As applications, the controversies about
the Behrens-Fisher problem and the comparison of two
binomials (2 × 2 tables) are considered from the
present point of view.
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