  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
principles. The main focus is on the conditionality principle that is probably
the most basic and less familiar among the three. The appendix discusses the
invariance principle and the conditionality principle in the case of sampling
from a finite population.
