Problems

• We want to conclude something about the signal but our data tends to be mostly background.

  • D'Agostini proposed that in the absence of a significant signal one should quote a sensitivity bound (where the LR falls below an agreed standard value) - a very pessimistic least common denominator.

• Clifford, Cousins, D'Agostini,... - most of us think P(theory|data) even for frequentist conf. intervals based on P(data|theory).

• Clifford: My suggestion for a possible way forward is to investigate and focus on frequentist confidence intervals which are approximate Bayesian credible intervals, or equivalently look for Bayesian credible intervals which have approximately the required coverage probability.

  • Even our beloved SM is an approximation!