  Statistical analysis is often used to evaluate the evidence for or against
scientific hypotheses, and various statistics (e.g., p-values, likelihood
ratios, Bayes factors) are interpreted as measures of evidence strength. Here I
consider evidence measurement from the point of view of representational
measurement theory, and argue that familiar evidence statistics do not conform
to any legitimate measurement scale type. I then consider the notion of an
absolute scale for evidence measurement, in a sense to be defined, focusing
particularly on the notion of absolute 0 evidence, which turns out to be
something other than what one might have expected.
