Data come in many forms. A survey might capture a snapshot of a person’s political views. A long-form interview might gather insights into parenting strategies. Ethnographic work by an embedded researcher might uncover patterns in a subculture that take months to piece together. (Consider Evicted, by Matthew Desmond.) Census data may capture information on someone’s level of education and income. Despite the distinct insights and strengths of each kind of data, it’s hard to deny that quantitative data are the crème de la crème of today’s data hierarchy. Some call it “hard” data, comparing it to “soft” data in a way that inevitably makes it sounds like the sensible older (perhaps colder?) brother. There is something about a number that seems so, well, certain. When a statement contains a statistic, it contributes to the “evidence base.”
It’s ironic, then, that the study of statistics is actually built on the notion that everything is uncertain. As I teach it to students interested in public service and policy analysis, I must work to help them realize that statistical analysis is both a powerful lens for understanding a situation and, simultaneously, rarely able to definitively answer our most interesting questions. It turns out that answering questions with some degree of uncertainty is often the best we can do. While this may disappoint, it should not surprise us! In fact, I believe this quality puts statistics squarely in step with the social sciences and humanities, recognizing that human questions are complex and few outcomes are inevitable.
But statistics gives us an important distinctive that, on its better days, can make it particularly compelling: in statistics, the expression of the degree of uncertainty is actually made explicit.