 Mayo (1996: Ch. 9) defends a predictivist-like position attributed to Neyman-Pearson statistical methods—the prohibition on after-trial constructions of hypotheses. To illustrate: Kish (1959) describes a study that investigated the statistical relationship between a large number of infant training experiences (nursing, toilet training, weaning, etc.) and subsequent personality and behavioral traits (e.g., school adjustment, nail biting, etc.) The study found a number of high correlations between certain training experience and later traits. The problem was that the study investigated so many training experiences that it was quite likely that some correlations would appear in the data simply by chance—even if there would ultimately prove to be no such correlation. An investigator who studied many possible correlations thus could survey that data and simply look for statistically significant differences and proclaim evidence for correlations despite such evidence being misleading—thus engaging in the dubious practice of the ‘after-trial construction of  hypothesis’.[10]  Mayo notes that such hypotheses should not count as having passed a severe test, thus she endorses the Neyman-Pearson prohibition on such construction. Hitchcock and Sober (2004) note that Mayo’s definition of severity as applied in this case differs from the one she employs in dealing with cases like her SAT example; Mayo (2008) replies at length to their criticism and argues that while she does employ two versions of the severity definition they nonetheless reflect a unified conception of severity.