在統計學中，效應值（英語：effect size，或譯效果量）是量化現象強度的數值。[1]效應值實際的統計量包括了兩個變數間的相關程度、迴歸模型中的迴歸係數、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。效應值與特效檢定的概念是互補的。在估算統計檢定力、需要的樣本數與進行統合分析時，效應值經常扮演重要角色。
在研究結果中給出效應值被視為恰當的或必須的。[2][3]相對於統計學上的顯著性，效應值有利於了解研究結果的強度。[4]特別是在社會科學和醫學研究上，效應值更顯得重要。絕對與相對效應值可以遞移不同的訊息，又可互相補充訊息。有個心理學的研究學會鼓勵學者給出效應值：
報告主要結果時必須一併報導效應值……如果測量值的單位在實際面上是有意義的（例如每人每日抽菸的香菸根數），則我們建議採用非標準化的效應值（例如迴歸係數或平均值差異）而不是標準化的效應值（例如相關係數）。
— L. Wilkinson and APA Task Force on Statistical Inference (1999, p. 599)
在比較平均數的情況下，效應值經常指的就是實驗結束後，實驗組與對照組之間「標準化後的平均差異程度」，依照慣例，效應值可解讀為以下幾個程度：
More information 效應值, d[5] ...
| 效應值
|
d[5]
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r[6]
|
| 較小
|
0.2
|
0.10
|
| 中等
|
0.5
|
0.30
|
| 較大
|
0.8
|
0.50
|
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