To judge by debates over distant reading, literary scholars might have something in common with the unadaptable tutor in george Eliot's The Mill on the Floss, the Rev. Mr. Stelling, who applies the same educational method to any boy in his charge: he sees Tom Tulliver's mind as a field to be plowed with the classics, not as a stomach that can't digest them. But if we look more closely at the range of projects in literary studies, from digital humanities to the new formalist poetics, we find few hidebound Stellings. Applying one's favorite method to all sizes and shapes of data is an obvious genre of error, and scholars and critics of all sorts studiously avoid it. In a maxim now popular among practitioners of digital humanities, when you have a hammer, everything looks like a nail. The humorous warning not to become enamored of shiny tools and ply them regardless of object (a schoolboy, all schoolboys) can come from people who use MALLET (MAchine Learning for LanguagE Toolkit) or other software to produce statistical analyses of words or of white spaces in nineteenth-century newspapers. As researchers who are primed to question the prescriptive bias of our doxa, our samples, our models, and our tools, we nevertheless have incentives to overstate the predictable errors of competing approaches.