统计显著性 [Statistical significance]
定义: 统计显著性描述的是使用零假设显著性检验（Null Hypothesis Significance Testing，简称NHST）得到的结果的一种属性，即在给定显著性水平（α）下，观测结果被认为在零假设成立时发生的概率极低。如 Tenny 和 Abdelgawad（2017, p.1）所述，它衡量的是“在零假设为真的条件下，获得当前数据或更极端数据的概率，与预设的可接受不确定性水平相比较的度量”。不同应用领域对显著性阈值的设定标准存在差异，但最终取决于研究者对适当误差范围的考量。美国统计协会 强调：“p 值常被误解为对零假设真伪的判断或观测数据由随机变异产生的概率。实则不然。p 值仅是数据与特定统计模型（零假设）间的关系的表述，而非对假设本身的直接论断”（Wasserstein & Lazar, 2016, p.131）。
相关术语: Alpha error, Frequentist statistics, Null hypothesis, Null Hypothesis Significance Testing (NHST), *P*\-value, Type I error **Incorrect definition:** Statistical significance describes the likelihood of the observed result against chance (regardless of the null hypotheses)
参考文献:
- Cassidy, S. A., Dimova, R., Giguère, B., Spence, J. R., & Stanley, D. J. (2019). Failing grade: 89% of introduction-to-psychology textbooks that define or explain statistical significance do so incorrectly. Advances in Methods and Practices in Psychological Science, 2(3), 233–239. https://doi.org/10.1177/2515245919858072
- Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70, 129–133. https://doi.org/10.1080/00031305.2016.1154108
原稿作者: Alaa AlDoh; Flávio Azevedo
审阅者: James E. Bartlett, Alexander Hart, Annalise A. LaPlume, Charlotte R. Pennington, Graham Reid, Timo Roettger, Suzanne L. K. Stewart
翻译者: AI-driven translation tool "TransFlow" (developed by Jinbiao Yang and COSN OpenTransfer team)
译稿审阅者: Yu Xu, Liangjie Chen, Ruoting Liu, Xiujuan Wen, Shuxian Jin