In this essay the general nature of conventional mathematical statistics is discussed. It is suggested that Neyman–Pearson–Wald decision theory is ineffective and that the making of terminal decisions must involve some sort of Bayesian process. Bayesian theory is, however, deficient with respect to obtaining of a prior distribution which seems to be a data analysis problem and to be the basic inference problem in a decision context. The relevance of parametric theory to the finite population inference problem is questioned. It is considered that intrinsic aspects of the logic of inference are given by the cases of populations of size one and two, and these are discussed. The role of labeling is considered, and some work of Hartley and Rao discussed. It is suggested that there are difficulties in unequal probability sampling with respect to the ultimate inferences, that is, beyond the matters of estimation and variance of estimators. It is suggested that admissibility theory is ineffective. Absence of attention to pivotality is deplored.