In her 1996 *Error and the Growth of Experimental Knowledge*, Professor Mayo argued against the Likelihood Principle on the grounds that it does not allow one to control long-run error rates in the way that frequentist methods do. This argument seems to me the kind of response a frequentist should give to Birnbaum’s proof. It does not require arguing that Birnbaum’s proof is unsound: a frequentist can accommodateBirnbaum’s conclusion (two experimental outcomes are evidentially equivalent if they have the same likelihood function) by claiming that respecting evidential equivalence is less important than achieving certain goals for which frequentist methods are well suited.

More recently, Mayo has shown that Birnbaum’s premises cannot be reformulated as claims about what sampling distribution should be used for inference while retaining the soundness of his proof. It does not follow that Birnbaum’s proof is unsound because Birnbaum’s original premises are not claims about what sampling distribution should be used for inference but instead as sufficient conditions for experimental outcomes to be evidentially equivalent.

Mayo acknowledges that the premises she uses in her argument against Birnbaum’s proof differ from Birnbaum’s original premises in a recent blog post in which she distinguishes between “the Sufficient Principle (general)” and “the Sufficiency Principle applied in sampling theory.“ One could make a similar distinction for the Weak Conditionality Principle. There is indeed no way to formulate Sufficiency and Weak Conditionality Principles “applied in sampling theory”that are consistent and imply the Likelihood Principle. This fact is not surprising: sampling theory is incompatible with the Likelihood Principle!

Birnbaum himself insisted that his premises were to be understood as “equivalence relations” rather than as “substitution rules” (i.e., rules about what sampling distribution should be used for inference) and recognized the fact that understanding them in this way was necessary for his proof. As he put it in his 1975 rejoinder to Kalbfleisch’s response to his proof, “It was the adoption of an unqualified equivalence formulation of conditionality, and related concepts, which led, in my 1972 paper, to the monster of the likelihood axiom” (263).

Because Mayo’s argument against Birnbaum’s proof requires reformulating Birnbaum’s premises, it is best understood as an argument not for the claim that Birnbaum’s original proof is invalid, but rather for the claim that Birnbaum’s proof is valid only when formulated in a way that is irrelevant to a sampling theorist. Reformulating Birnbaum’s premises as claims about what sampling distribution should be used for inference is the only way for a fully committed sampling theorist to understand them. Any other formulation of those premises is either false or question-begging.

Mayo’s argument makes good sense when understood in this way, but it requires a strong prior commitment to sampling theory. Whether various arguments for sampling theory such as those Mayo gives in *Error and the Growth of Experimental Knowledge* are sufficient to warrant such a commitment is a topic for another day. To those who lack such a commitment, Birnbaum’s original premises may seem quite compelling. Mayo has not refuted the widespread view that those premises do in fact entail the Likelihood Principle.

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