Far East Journal of Theoretical Statistics
Volume 21, Issue 1, Pages 97 - 111
(January 2007)
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TESTS OF SIGNIFICANCE VS TESTS OF HYPOTHESIS
L. Prieto (Spain), I. Herranz (Spain), M. Martínez-Sellés (Spain) and R. Alonso (Spain)
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Abstract: For most researchers the interpretation of statistical tests P
value is the eternal pending subject. In spite of attending courses, buying
books, and reading articles the confusion persists. Most end up trusting
compulsively in the "5% barrier", assuming that
guarantees that the result found
in the sample is a universal truth. They can pass from desolation to euphoria if
the P value of their study changes, for example, from 0.053 to 0.048.
How has been this simplification transformed into a dogma
that confuses researchers and threatens with logical process of statistical
inference for decades? The blame is distributed in equal parts between two
implied communities:
Researchers take refuge in this rule that exempts them from
reasoning and allows them to publish the conclusions of their works with
seemingly convincing sentences.
Many statisticians confuse decision-making with acquisition
of knowledge and translate this confusion to researchers by teaching them rules
to make decisions where there is nothing to decide.
It depends on us statisticians that thousands of researchers continue using
these ridiculous formulas or, on the contrary, are able to understand
statistical inference without ambiguity and to apply concepts correctly. The
disagreement should cease among those in favour of the tests of significance [4]
and the Tests of Hypothesis [8]. All of them must be aware that both approaches
are valid, each one in the context for which it was designed. And researchers,
who do not know statistical inference and depend in this aspect on the
statistician, should be taught the difference between acquiring knowledge and
making decisions. |
Keywords and phrases: Keywords and phrases: P value, statistical inference,
5% criterion, taking decisions. |
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