 
Summary: Getting to know your probabilities:
Three ways to frame personal probabilities for decision making.
Teddy Seidenfeld CMU
An old, wise, and widely held attitude in Statistics is that modest
intervention in the design of an experiment followed by simple
statistical analysis may yield much more of value than using very
sophisticated statistical analysis on a poorly designed existing data set.
In this sense, good inductive learning is active and forward looking, not
passive and focused exclusively on analyzing what is already given.
In this talk I review three different approaches for how a decision
maker might actively frame her/his probability space rather than being
passive in that phase of decision making.
Method 1: Assess precise/determinate probabilities only for the set of
random variables that define the decision problem at hand. Do not
include other "nuisance" variables in the space of possibilities. In this
sense, overrefining the space of possibilities may make assessing
probabilities infeasible for good decision making.
Example 1.1:
Random sampling: the "nuisance" of individual tags and
designing an experiment to prove. (KS, 1990).
