在統計學中，效應值（Effect size）是量化現象強度的數值。效應值實際的統計量包括了二個變數間的相關程度、回歸模型中的回歸係數、不同處理間平均值的差異……等等。無論哪種效應值，其絕對值越大表示效應越強，也就是現象越明顯。效應值與特效檢驗的概念是互補的。在估算統計檢定力、需要的樣本數與進行元分析時，效應值經常扮演重要角色。
什麼時候用標準化的？什麼時候用非標準化？
如果變量的單位是有意義的，比如時長、身高、收入等等，建議匯報非標準化的效應值，結果可解釋為每一小時/一釐米/1元/1000元的變化可以帶來因變量c的變化，其中直接引起的變化為c',被中介的作用為ab.
如果變量的單位沒有實際意義，比如生活質量、婚姻生活滿意度、幸福感等，建議匯報標準化的效應值，這樣一來，可以比較不同自變量效應的大小。
The term effect size can refer to a standardized measure of effect (such as r, Cohen's d, or the odds ratio), or to an unstandardized measure (e.g., the difference between group means or the unstandardized regression coefficients). Standardized effect size measures are typically used when:
the metrics of variables being studied do not have intrinsic meaning (e.g., a score on a personality test on an arbitrary scale),
results from multiple studies are being combined,
some or all of the studies use different scales, or
it is desired to convey the size of an effect relative to the variability in the population.
In meta-analyses, standardized effect sizes are used as a common measure that can be calculated for different studies and then combined into an overall summary.
About 50 to 100 different measures of effect size are known.
Correlation Family: Effect Sizes Based On "Variance Explained"
These effect sizes estimate the amount of the variance within an experiment that is "explained" or "accounted for" by the experiment's model.
Pearson R Or Correlation Coefficient
Pearson's correlation, often denoted r and introduced by Karl Pearson, is widely used as an effect size when paired quantitative data are available; for instance if one were studying the relationship between birth weight and longevity. The correlation coefficient can also be used when the data are binary. Pearson's r can vary in magnitude from −1 to 1, with −1 indicating a perfect negative linear relation, 1 indicating a perfect positive linear relation, and 0 indicating no linear relation between two variables. Cohen gives the following guidelines for the social sciences:
A related effect size is r, the coefficient of determination (also referred to as R or "r-squared"), calculated as the square of the Pearson correlation r. In the case of paired data, this is a measure of the proportion of variance shared by the two variables, and varies from 0 to 1. For example, with an r of 0.21 the coefficient of determination is 0.0441, meaning that 4.4% of the variance of either variable is shared with the other variable. The r is always positive, so does not convey the direction of the correlation between the two variables.
Eta-squared describes the ratio of variance explained in the dependent variable by a predictor while controlling for other predictors, making it analogous to the r. Eta-squared is a biased estimator of the variance explained by the model in the population (it estimates only the effect size in the sample). This estimate shares the weakness with r that each additional variable will automatically increase the value of η. In addition, it measures the variance explained of the sample, not the population, meaning that it will always overestimate the effect size, although the bias grows smaller as the sample grows larger.
This form of the formula is limited to between-subjects analysis with equal sample sizes in all cells. Since it is less biased (although not unbiased), ω is preferable to η; however, it can be more inconvenient to calculate for complex analyses. A generalized form of the estimator has been published for between-subjects and within-subjects analysis, repeated measure, mixed design, and randomized block design experiments. In addition, methods to calculate partial ω for individual factors and combined factors in designs with up to three independent variables have been published.