 Eliminativism is most credible when experimentation is at issue (see, e.g., Guala 2012). Indeed, the appeal to Bacon’s idea of crucial experiment (instantia crucis) and related notions (e.g., “severe testing”) is a fairly reliable mark of eliminativist inclinations. Experimentation is, to a large extent, precisely an array of techniques to keep undesired interfering factors at a minimum by active manipulation and deliberate control (think of the blinding procedure in medical trials, with \(h\) the hypothesized effectiveness of a novel treatment and \(e\) a relative improvement in clinical endpoints for a target subsample of patients thus treated). When this kind of control obtains, popular statistical tools are supposed to allow for the calculation of the probability of \(e\) in case \(h\) is false meant as a “relative frequency in a (real or hypothetical) series of test applications” (Mayo 1991, 529), and to secure a sufficiently low value to validate the positive outcome of the test. It is much less clear how firm a grip this approach can retain when inference takes place at higher levels of generality and theoretical commitment, where the hypothesis space is typically much too poorly ordered to fit routine error-statistical analyses. Indeed, Laudan (1997, 315; also see Musgrave 2010) spotted in this approach the risk of a “balkanization” of scientific reasoning, namely, a restricted focus on scattered pieces of experimental inference (but see Mayo 2010 for a defense). 