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Rejecting the null hypothesis can tell a researcher that a finding is statistically significant (for example,...

Rejecting the null hypothesis can tell a researcher that a finding is statistically significant (for example, say there is a significant difference between students in our class and students in general) -- but this doesn’t tell us how extreme the difference really is (just that there is one). What statistical metric tells us how extreme a result is? (Or, in other words, the extent to which two population distributions do not overlap)?

A. Effect size / Cohen’s D B. The critical Z score (Zcrit) C. Statistical Power D. Confidence Intervals

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The answer here is -> Effect size/Cohen's D, since, by definition, the effect size is a quantitative measure by which we can quantify the significant difference between two population distributions. It emphasizes upon the size of this difference alone.

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