The most common assumption for the parametric tests is that the assumption of normality. Typically, the assumption of normality applies to the sampling distribution, rather than the underlying data. This is good news since it is usually satisfied for sufficient large data sets (. N > 30) due to the central limit theorem. In general, normality of the underlying data is sufficient, but not necessary, for the use of parametric tests.
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In order to test a claim scientifically, it must be possible that the claim could also be proven false. One of the hallmarks of a pseudoscience is that it makes claims that cannot be refuted or proven false.
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