Examples are: discussing observed differences and associations even if they are not statistically significant (the often used expression is “borderline significance”); discussing differences which are statistically significant but are not clinically meaningful; drawing conclusions about the causality, even if the study was not designed as an experiment; drawing conclusions about the values outside the range of observed data (extrapolation); overgeneralization of the study conclusions to the entire general population, even if a study was confined to the population subset; Type I (the expected effect is found significant, when actually there is none) and type II (the expected effect is not found significant, when it is actually present) errors ( 6 ).
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