  The primary objective of this paper is to revisit and make a case for the
merits of R.A. Fisher's objections to the decision-theoretic framing of
frequentist inference. It is argued that this framing is congruent with the
Bayesian but incongruent with the frequentist inference. It provides the
Bayesian approach with a theory of optimal inference, but it misrepresents the
theory of optimal frequentist inference by framing inferences solely in terms
of the universal quantifier `for all values of theta in the parameter space'.
This framing is at odds with the primary objective of model-based frequentist
inference, which is to learn from data about the true value of theta (unknown
parameter(s)); the one that gave rise to the particular data. The frequentist
approach relies on factual (estimation, prediction), as well as hypothetical
(testing) reasoning whose primary aim is to learn from data about the true
theta. The paper calls into question the appropriateness of admissibility and
reassesses Stein's paradox as it relates to the capacity of frequentist
estimators to pinpoint the true theta. The paper also compares and contrasts
loss-based errors with traditional frequentist errors, such as coverage, type I
and II; the former are attached to {\theta}, but the latter to the inference
procedure itself.
