Measuring statistical evidence using relative belief

Publication date: Available online 7 January 2016 Source:Computational and Structural Biotechnology Journal Author(s): Michael Evans A fundamental concern of a theory of statistical inference is how one should measure statistical evidence. Certainly the words “statistical evidence,” or perhaps just “evidence,” are much used in statistical contexts. It is fair to say, however, that the precise characterization of this concept is somewhat elusive. Our goal here is to provide a definition of how to measure statistical evidence for any particular statistical problem. Since evidence is what causes beliefs to change, it is proposed to measure evidence by the amount beliefs change from a priori to a posteriori. As such, our definition involves prior beliefs and this raises issues of subjectivity versus objectivity in statistical analyses. This is dealt with through a principle requiring the falsifiability of any ingredients to a statistical analysis. These concerns lead to checking for prior-data conflict and measuring the a priori bias in a prior.

Source: Computational and Structural Biotechnology Journal - January 11, 2016 Category: Biotechnology Source Type: research