**D**eborah Mayo arXived a Statistical Science paper a few days ago, along with discussions by Jan Bjørnstad, Phil Dawid, Don Fraser, Michael Evans, Jan Hanning, R. Martin and C. Liu. I am very glad that this discussion paper came out and that it came out in Statistical Science, although I am rather surprised to find no discussion by Jim Berger or Robert Wolpert, and even though I still cannot entirely follow the deductive argument in the rejection of Birnbaum’s proof, just as in the earlier version in Error & Inference. But I somehow do not feel like going again into a new debate about this critique of Birnbaum’s derivation. (Even though statements like the fact that the SLP “would preclude the use of sampling distributions” (p.227) would call for contradiction.)

*“It is the imprecision in Birnbaum’s formulation that leads to a faulty impression of exactly what is proved.” M. Evans*

Indeed, at this stage, I fear that [for me] a more relevant issue is whether or not the debate does matter… At a logical cum foundational [and maybe cum historical] level, it makes perfect sense to uncover if and which if any of the myriad of Birnbaum’s likelihood Principles holds. [Although trying to uncover Birnbaum's motives and positions over time may not be so relevant.] I think the paper and the discussions acknowledge that *some* version of the weak conditionality Principle does not imply *some* version of the strong likelihood Principle. With other logical implications remaining true. At a methodological level, I am less much less sure it matters. Each time I taught this notion, I got blank stares and incomprehension from my students, to the point I have now stopped altogether teaching the likelihood Principle in class. And most of my co-authors do not seem to care very much about it. At a purely mathematical level, I wonder if there even is ground for a debate since the notions involved can be defined in various imprecise ways, as pointed out by Michael Evans above and in his discussion. At a statistical level, sufficiency eventually is a strange notion in that it seems to make plenty of sense until one realises there is no interesting sufficiency outside exponential families. Just as there are very few parameter transforms for which unbiased estimators can be found. So I also spend very little time teaching and even less worrying about sufficiency. (As it happens, I taught the notion this morning!) At another and presumably more significant statistical level, what matters is information, e.g., conditioning means adding information (i.e., about which experiment has been used). While complex settings may prohibit the use of the entire information provided by the data, at a formal level there is no argument for not using the entire information, i.e. conditioning upon the entire data. (At a computational level, this is no longer true, witness ABC and similar limited information techniques. By the way, ABC demonstrates if needed why sampling distributions matter so much to Bayesian analysis.)

*“Non-subjective Bayesians who (…) have to live with some violations of the likelihood principle (…) since their prior probability distributions are influenced by the sampling distribution.” D. Mayo (p.229)*

In the end, the fact that the prior may depend on the form of the sampling distribution and hence does violate the likelihood Principle does not worry me so much. In most models I consider, the parameters are endogenous to those sampling distributions and do not live an ethereal existence independently from the model: they are substantiated and calibrated by the model itself, which makes the discussion about the LP rather vacuous. See, e.g., the coefficients of a linear model. In complex models, or in large datasets, it is even impossible to handle the whole data or the whole model and proxies have to be used instead, making worries about the structure of the (original) likelihood vacuous. I think we have now reached a stage of statistical inference where models are no longer accepted as ideal truth and where approximation is the hard reality, imposed by the massive amounts of data relentlessly calling for immediate processing. Hence, where the self-validation or invalidation of such approximations in terms of predictive performances is the relevant issue. Provided we can at all face the challenge…