Compatibility ranges as a practical alternative to the “significant/non-significant” statistical dichotomy
Science can be defined as a social system built on the concept of critical agreement on evidentiary states. The latter must be achieved through rational thinking, communication, and the so-called ‘scientific method’, which involves a series of procedures aimed at ensuring the replicability of invest...
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| Format: | Article |
| Language: | English |
| Published: |
European Publishing
2024-06-01
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| Series: | Public Health and Toxicology |
| Subjects: | |
| Online Access: | https://www.publichealthtoxicology.com/Compatibility-ranges-as-a-practical-alternative-to-the-significant-non-significant,189530,0,2.html |
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| Summary: | Science can be defined as a social system built on the concept
of critical agreement on evidentiary states. The latter must
be achieved through rational thinking, communication,
and the so-called ‘scientific method’, which involves a
series of procedures aimed at ensuring the replicability of
investigative experiments. In this regard, the dichotomization
of statistical results into ‘significant’ and ‘non-significant’
has led to a long series of replication failures and fostered
the misleading expectation that a mere numerical criterion
can replace analytical reasoning. Especially in fields like
toxicology and public health, such misuse can have serious
consequences. Indeed, no study can prove that a result is
(not) significant since uncertainty is always part of scientific
research. At most, based on the above considerations and a
comprehensive analysis of costs, risks, and benefits, it can be
decided whether a certain phenomenon meets the threshold
of scientific evidence required to undertake concrete actions.
In light of this, the present manuscript proposes and
discusses alternative concepts to statistical dichotomies,
such as ranges of compatibility and effect size. Furthermore,
it emphasizes the necessity to investigate the compatibility of
the experimental data with all the relevant target hypotheses
(not just the null one) and all the background assumptions.
Finally, it proposes a compact framework for a complete
presentation of results, including effect size. In this regard,
the adoption of multiple confidence/compatibility intervals
or surprisal intervals is recommended. |
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| ISSN: | 2732-8929 |