<Table> <Tr> <Td> </Td> <Td> This article may be too technical for most readers to understand . Please help improve it to make it understandable to non-experts, without removing the technical details . (February 2014) (Learn how and when to remove this template message) </Td> </Tr> </Table> <Tr> <Td> </Td> <Td> This article may be too technical for most readers to understand . Please help improve it to make it understandable to non-experts, without removing the technical details . (February 2014) (Learn how and when to remove this template message) </Td> </Tr> <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> Reporting effect sizes or estimates thereof (effect estimate (EE), estimate of effect) is considered good practice when presenting empirical research findings in many fields . The reporting of effect sizes facilitates the interpretation of the substantive, as opposed to the statistical, significance of a research result . Effect sizes are particularly prominent in social science and in medical research (where size of treatment effect is important). Relative and absolute measures of effect size convey different information, and can be used complementarily . A prominent task force in the psychology research community made the following recommendation: </P>

In addition to hypothesis testing effect size calculations are recommended as a way to assess