Categorical and nominal variables decrease statistical power and increase the needed sample size to detect significant effects. This is due to categorical measurement possessing decreased precision and accuracy.
Less powerful non-parametric statistics are used with categorical outcomes. Less precision and accuracy of treatment effects is yielded from non-parametric statistics as well.
Categorical outcomes are very prevalent in medicine, so researchers should plan to either measure for large effect sizes or collect larger sample sizes to achieve adequate statistical power.
Less powerful non-parametric statistics are used with categorical outcomes. Less precision and accuracy of treatment effects is yielded from non-parametric statistics as well.
Categorical outcomes are very prevalent in medicine, so researchers should plan to either measure for large effect sizes or collect larger sample sizes to achieve adequate statistical power.
Categorical outcomes decrease statistical power and increase the needed sample size.
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