When I think of data analysis and graphing, the first thing to come to mind is certainly not English literature. I think of my experimental psych class, or psych statistics. I would associate math with graphs long before I would think of novels or authors. Yet the purposes of using graphs in this context is quite similar to using graphs in any of the previously mentioned areas. The purpose is to organize information into shapes and structures, to show similarities and differences. It is interesting to consider this shift of perspective. Thus far, we have been extremely analytical and critical. We’ve looked at the structure of a text, or lack-there-of, looking deer and narrowly into very specific ideas. Instead of using a qualitative approach, we are using a quantitative one. This chapter of Moretti’s deals with seeing the bigger picture. Moretti claims that a field this large “isn’t a sum of individual cases” (pg. 4). He uses the graphs to display this information. A struggle that exists throughout this chapter, concerns a claim Moretti makes, but then goes on to question. On page 9, he claims that quantitative data “provides a type of data which is ideally independent of interpretations”. Graphs themselves can only provide data, not interpretations. A graph can depict a trend such as an increase or a decrease in the use of a title, however the graph cannot explain why. This is conflicting as a graph can only fill half of a greater picture. On page 30, Moretti addresses this by stating that the challenge of quantitative data is that they simply must be interpreted. The problem is that this interpretation is above and beyond that of the graphs quantitative depiction. Research psychology classes are constantly informing researchers about potential flaws in their data. The biggest warning is that correlation does not equal causation. This tells us that just because two variables are increasing at the same time, they are not necessarily causing each other to do so. Just because a town has a lot of libraries and a high crime rate, it is likely that the two have nothing to do with each other. There is often a third variable, confounding itself over the results. Therefore, interpretation is extremely important. It’s not enough to address a pattern. It is necessary to transcend the realms of these quantitative data, and find not only the correlation, but the interpretation of the causation as well.