Statistical power and extensive variance of effect size
Extensive variance of outcome decreases statistical power and increases the needed sample size
Extensive variance in the outcome means that more observations will be needed to detect differences in independent groups or within-subjects. Extensive variance or heterogeneity in a distribution means that the population is diverse or widely dispersed. Overestimating the variance in an outcome is a good practice because researchers are forced to collect more observations, leading to more precise measures of treatment effect.
When working with heterogeneous populations (extensive variance or variability), more observations of the outcome will allow for the diversity of the population to be truly represented. Researchers stand a much better chance of making more valid inferences when more observations of an outcome are collected.
When working with heterogeneous populations (extensive variance or variability), more observations of the outcome will allow for the diversity of the population to be truly represented. Researchers stand a much better chance of making more valid inferences when more observations of an outcome are collected.
Extensive variance in an effect leads to decreased statistical power and increases the needed sample size.
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