 in statistics, an effect size' is a quantitative measure of the strength of a phenomenon. examples of effect sizes are the correlation between two variables, the regression coefficient, 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 each type of effect-size, a larger absolute value always indicates a stronger effect. effect sizes complement statistical hypothesis testing, and play an important role in statistical power analyses, sample size planning, and in meta-analyses. especially in meta-analysis, where the purpose is to combine multiple effect-sizes, the standard error of effect-size is of critical importance. the s.e. of effect-size is used to weight effect-sizes when combining studies, so that large studies are considered more important than small studies in the analysis. the s.e. of 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 (ns). reporting effect sizes 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 and medical research. relative and absolute measures of effect size convey different information, and can be used complementarily. a prominent task force in the psychology research community expressed the following recommendation: {{quotation|always present effect sizes for primary outcomes...if the units of measur
Clinical_research Educational_psychology_research_methods Effect_size Hypothesis_testing Medical_statistics Meta-analysis Pharmaceutical_industry