  This article gives a survey of the e-value, a statistical significance
measure a.k.a. the evidence rendered by observational data, X, in support of a
statistical hypothesis, H, or, the other way around, the epistemic value of H
given X. The $e$-value and the accompanying FBST, the Full Bayesian
Significance Test, constitute the core of a research program that was started
at IME-USP, is being developed by over 20 researchers worldwide, and has, so
far, been referenced by over 200 publications.
  The e-value and the FBST comply with the best principles of Bayesian
inference, including the likelihood principle, complete invariance, asymptotic
consistency, etc. Furthermore, they exhibit powerful logic or algebraic
properties in situations where one needs to compare or compose distinct
hypotheses that can be formulated either in the same or in different
statistical models. Moreover, they effortlessly accommodate the case of sharp
or precise hypotheses, a situation where alternative methods often require ad
hoc and convoluted procedures. Finally, the FBST has outstanding robustness and
reliability characteristics, outperforming traditional tests of hypotheses in
many practical applications of statistical modeling and operations research.
