Abstract Sufficiency, conditionality, and invariance are basic principles of statistical inference. Current mathematical statistics courses do not devote much teaching time to these classical principles, and even ignore the latter two, in order to teach modern methods. However, being the philosophical cornerstones of statistical inference, a minimal understanding of these principles should be part of any curriculum in statistics. The scaled uniform model is used here to demonstrate the importance and usefulness of the conditionality principle, which is probably the most basic and less familiar among the three.

中文翻译：

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重新审视缩放均匀模型

摘要 充分性、条件性和不变性是统计推断的基本原则。目前的数理统计课程并没有在这些经典原理上投入太多的教学时间，甚至忽略了后两者，以教授现代方法。然而，作为统计推理的哲学基石，对这些原则的最低限度的理解应该是任何统计学课程的一部分。这里使用缩放的均匀模型来证明条件原则的重要性和有用性，这可能是三者中最基本和不太熟悉的。