 But Mayo notes that ‘no matter what’ can be interpreted in two ways: (a) no matter what the data are, and (b) no matter whether H is true or false. (1) is true when interpreted as (a), but in order to establish that accommodated evidence fails to support H (as Giere intends) (1) must be interpreted as (b). However, (1) is false when so interpreted. Mayo (1996: 271) illustrates this with a simple example: let the evidence e be a list of SAT scores from students in a particular class. Use this evidence to compute the average score x, and set h = the mean SAT score for these students is x. Now of course h has been use-constructed from e. It is true that whatever mean score was computed would fit the data no matter what the data are—but hardly true that h would have fit the evidence no matter whether h was true or false. If h were false it would not fit the data, because the data will inevitably fit only a true hypothesis. Thus h has passed a maximally severe test: it is virtually impossible for h to fit the data if h is false—despite the fact that h is built to fit e. 