Effect size is a statistical concept that is used to measure the strength of the relationship between two variables on a numeric scale. This metric is used in statistical testing of the null hypothesis (usually that the effect is zero). Metrics for effect size may be in some form of physical unit, such as differences in blood pressure or blood glucuse level. More commonly they are ‘unit free’, such as Pearson’s correlation co-efficient (r), standardised difference of means or Cohen’s d, and coefficients in regression equations. For binary data relative risk (and relative risk reduction) is frequently used for effect size in clinical trials, and odds ratios are especially useful for combining the results of many studies in meta-analyses. In hypothesis testing, effect size, power (1–b), sample size, and critical significance level (a) are related to each other: so, for example, the desired effect size to be detected, combined with a and b may be used to calculate the sample size for a clinical study.

How to cite: Effect Size [online]. (2016). York; York Health Economics Consortium; 2016. https://yhec.co.uk/glossary/effect-size


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