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In statistical hypothesis testing
and power analysis, an effect size is the size
of a statistically significant treatment effect – that is,
a difference between a mathematical characteristic (often the mean) of a distribution of a
dependent variable associated with a specific level of an
independent variable and the same characteristic of all
distributions defined by different levels of the independent variable. For example, if the mean of the mean scores of all
experimental groups taking a test is 100, one of the groups is defined as consisting of men, and men's mean score is 102, the
treatment effect for men is 102 - 100 = 2.
Since dependent variables are measured on many different scales, analysis of effect size often requires converting them to
standard formats, for example:
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