In a previous post I suggested that historians should use quantitative methods less to answer existing questions than to pose new ones. Such a digital humanities (DH) approach would be the reverse of the older social science history approach, in which social science tools were use to “answer” definitively longstanding questions. This post offers another example of how data visualization can suggest new questions, and how social science and humanistic methods can be complementary in unexpected ways.
One way to conceptualize this complementarity is John Tukey’s observation that “data = smooth + rough,” or, in more common parlance, quantitative analysis seeks to separate patterns and outliers. In a traditional social science perspective, the focus is on the “smooth,” or the formal model, and the corresponding ability to make broad generalizations. Historians, by contrast, often write acclaimed books and articles on the “rough,” single exceptional cases. These approaches are superficially opposite, but there is an underlying symbiosis: we need to find the pattern before we can find the outliers.
Read More: Smooth and Rough on the Highways of France