Seminar: Michael Lavine

Many statistical analyses are characterized by how often a procedure works: how often an interval covers a true value, a null hypothesis is rejected, an item is correctly classified, etc. But assessing how often a procedure works differs from assessing the evidence in a data set.  Understanding the difference is prerequisite to understanding what matters in a given analysis: the procedure, the evidence, or both.

 

We begin with several examples that illustrate the difference between assessing evidence and assessing procedures. Then we work carefully through another example that explains the fundamental difference: assessing procedures requires and assessing evidence forbids averaging over other data sets.  Along the way we introduce foundational principles of evidence and inference, namely the Conditionality Principle (CP) and the Sufficiency Principle (SP), and show how CP and SP together imply the Likelihood Principle.