Reviews the interconnection between inductive logic and statistical inference. Terribly frequentist, and owes heavily to Neyman-Pearson conceptions of statistical science. While the explanations of the Kolmogorov axioms and Fisherian fiducialism (now since outdated in most statistical practice) are excellent, the explanation of Bayesianism and the "subjective" probabilists was quite thin. Hacking acknowledges such in the introduction, mentioning only being influenced by Savage's Foundation of Statistics for the Bayesians. As such, little about Cox's postulates, Jaynes's Boolean algebra-based probability system, and similar is crammed into two short chapters at the end of the book. Which was quite disappointing, because the clear-headed logical connection Hacking gave to frequentist and "objective" statistics would have been equally enlightening for Bayesian and "subjective" statistics.