 Determining the fit between inferential methods and data requires high levels of expertise and contextual judgement (a situation known within machine learning as the “no free lunch theorem”). Indeed, overreliance on software for inference and data modelling can yield highly problematic results. Symons and Horner note that the use of complex software in big data analysis makes margins of error unknowable, because there is no clear way to test them statistically (Symons &amp; Horner 2014: 473). The path complexity of programs with high conditionality imposes limits on standard error correction techniques. As a consequence, there is no effective method for characterising the error distribution in the software except by testing all paths in the code, which is unrealistic and intractable in the vast majority of cases due to the complexity of the code.