统计假设 [Statistical Assumptions]

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定义: 统计分析方法和模型通常假定数据具有某些特征（如统计独立性、样本随机性、正态性、方差齐性等）。在进行分析前必须审慎检验这些假设，因其一旦被违反，可能影响研究结果与结论。在开放和可重复科学中，根据验证的假设和检查或修正的结果，报告假设测试属于良好实践。

相关术语: Null Hypothesis Significance Testing (NHST), Statistical Significance, Statistical Validity, Transparency, Type I error, Type II error, Type M error, Type S error

参考文献:

  • Garson, G. D. (2012). Testing Statistical Assumptions (2012th ed.). North Carolina State University.
  • Hahn, G. J., & Meeker, W. Q. (1993). Assumptions for Statistical Inference. The American Statistician, 47(1), 1–11. https://doi.org/10.1080/00031305.1993.10475924
  • Hoekstra, R., Kiers, H., & Johnson, A. (2012). Are assumptions of well-known statistical techniques checked, and why (not)? Frontiers in Psychology, 3(137), 1–9. https://doi.org/10.3389/fpsyg.2012.00137

原稿作者: Graham Reid

审阅者: Jamie P. Cockcroft, Sam Parsons, Martin Vasilev, Julia Wolska

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