Why the Decision-Theoretic Perspective Misrepresents Frequentist Inference

The primary objective of this paper is to revisit a widely held view that decision theory provides a unifying framework for comparing the frequentist and Bayesian approaches. The paper calls into question this viewpoint and argues that the decision theoretic perspective misrepresents both the underlying reasoning and the primary objective of frequentist inference to learn from data about the true parameter ∗ This is primarily because of its reliance on loss functions in conjunction with the universal quantifier ‘for all values of ’. For the same reasons, the paper calls into question the appropriateness and judiciousness of admissibility and the James-Stein risk ‘optimality’ for frequentist estimation. These findings largely substantiate Fisher’s (1935; 1955) claims concerning the impertinence of loss functions in scientific inference and their appropriateness for ‘acceptance sampling’.