Given data, central issues that any theory of statistical inference has to handle, concerning a quantity of interest 
prescribed by the application, are the following: 

(i) What does the evidence in the data say is the best choice of a value for a quantity of interest (estimation)? 
(ii) Does a quantity of interest take a particular value (hypothesis assessment)? 
(iii) Furthermore, any such theory should also say something about how strong the evidence is. Just as an estimate 
      without an assessment of its accuracy is useless, hypothesis assessment calls for the quantification of evidential strength. 

Given the centrality of the evidence it seems natural that a characterization of how statistical evidence is to be measured 
should play a primary role in determining the theory of inference. The purpose of this session is to consider recently developed 
approaches to the development of a theory of statistical inference that include an explicit characterization of how statistical 
evidence is to be measured. Such a theory has the potential to remove many of the ambiguities/paradoxes that currently cause 
problems for the application of statistical methodology.

Organiser: Prof. Michael Evans 

Chair: Michael Evans 

Speaker: Prof. Lisa Strug 

Speaker: Prof. Daniel Roy 

Speaker: Prof. Michael Evans