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Biases (inferential error tendencies) can unconsciously affect inferring.
Thagard volunteers a list of more than 50 of these inferential error tendencies.
As the Harvard political scientist Ryan Enos pointed out, blaming the lack of cross-party socializing for polarization is a classic inferential error in which people confuse the manifestation of a phenomenon with the phenomenon itself.
However decision maker's preferences regarding acceptable levels of risks for inferential error (type I error) in economic data may vary.
The qualities of the microbiological methods of the studies were assessed to determine the extent to which bias is introduced and results in inferential error.
Specific psychometric study design features are therefore essential to establish and assess, for instance, controls that are put in place for systematic bias, non-systematic bias and inferential error.
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Appropriate accounting for the differences between measured and "pure" values will assist in minimising the ecological inferential errors associated with the use of stable isotope analysis.
In assessing treatment effects, both the randomized trial and the observational data base are subject to inferential errors due to multiple comparisons.
However, others have pointed out that d and r are not necessarily the most appropriate indices of effect size, that selection of an effect size index has to consider limitations and assumptions of using each specific index, and that mindless use of d or r may lead to substantial inferential errors [43].
To avoid inferential errors, gender specific analyses were performed (N men = 2884; N women = 3317).
Several sources of noise, including technical and procedural factors, may influence measurement quality, generating inferential errors.
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