<P> In statistics , an effect size is a quantitative measure of the magnitude of a phenomenon . Examples of effect sizes are the correlation between two variables , the regression coefficient in a regression , the mean difference , or even the risk with which something happens , such as how many people survive after a heart attack for every one person that does not survive . For most types of effect size , a larger absolute value always indicates a stronger effect , with the main exception being if the effect size is an odds ratio . Effect sizes complement statistical hypothesis testing , and play an important role in power analyses , sample size planning , and in meta - analyses . They are the first item ( magnitude ) in the MAGIC criteria for evaluating the strength of a statistical claim . Especially in meta - analysis , where the purpose is to combine multiple effect sizes , the standard error ( S.E. ) of the effect size is of critical importance . The S.E. of the effect size is used to weigh effect sizes when combining studies , so that large studies are considered more important than small studies in the analysis . The S.E. of the effect size is calculated differently for each type of effect size , but generally only requires knowing the study 's sample size ( N ) , or the number of observations in each group ( n 's ) . </P>
<P> In statistics , an effect size is a quantitative measure of the magnitude of a phenomenon . Examples of effect sizes are the correlation between two variables , the regression coefficient in a regression , the mean difference , or even the risk with which something happens , such as how many people survive after a heart attack for every one person that does not survive . For most types of effect size , a larger absolute value always indicates a stronger effect , with the main exception being if the effect size is an odds ratio . Effect sizes complement statistical hypothesis testing , and play an important role in power analyses , sample size planning , and in meta - analyses . They are the first item ( magnitude ) in the MAGIC criteria for evaluating the strength of a statistical claim . Especially in meta - analysis , where the purpose is to combine multiple effect sizes , the standard error ( S.E. ) of the effect size is of critical importance . The S.E. of the effect size is used to weigh effect sizes when combining studies , so that large studies are considered more important than small studies in the analysis . The S.E. of the effect size is calculated differently for each type of effect size , but generally only requires knowing the study 's sample size ( N ) , or the number of observations in each group ( n 's ) . </P>
in addition to hypothesis testing effect size calculations are recommended as a way to assess
in addition to hypothesis testing effect size calculations are recommended as a way to assess
the strength of a statistical claim
the strength of a statistical claim