在多變量的研究中，大多以統計的顯著性（p-value）來作為假設檢定的唯一指標。然而，由於統計顯著性容易受到樣本大小的影響，因此有許多學者指出，統計上的顯著性並不能等同於實務上的顯著性。換言之，統計上的顯著性並不適合做為推論假設檢定的唯一依據。實務上的顯著性可依賴「效果量」及其他指標來做為輔助判斷的準則。目前在教育領域的量化研究中有提到效果量的文章比例仍然偏低。有鑑於此，本文針對「效果量」在研究實務上的重要性、定義、衡量方式、解釋逐一探討。此外，本文並舉出正確判斷顯著性的其他注意事項，以提供國內學者在撰寫量化研究報告之研究結果時的參考。

The test of statistical significance has been a common tool in quantitative researches. Most researchers used p-value to explain what they found in the researches. Owing to Statistical significance are so heavily influenced by sample size. Many scholars suggest use effect size indices to assist researchers interpreting the result in the paper. The 1994 and 2001 American Psychological Association (APA) publication Manual incorporated important revisions “encouraging” authors to report effect sizes and “emphasizing” the importance of effect sizes. According to these reasons, this paper discusses about practical information on generating, reporting, and interpreting effect sizes. Finally, the article also contains some ways of practical significance determination. (Confidence interval and human value are also included.)