The Scaled Uniform Model Revisited

Micha Mandel*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageAmerican English
Pages (from-to)98-100
Number of pages3
JournalAmerican Statistician
Issue number1
StatePublished - 2 Jan 2020

Bibliographical note

Publisher Copyright:
© 2019, © 2019 American Statistical Association.


  • Conditionality principle
  • Minimal sufficient
  • Scale family
  • Uniformly minimum variance unbiased estimator


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