The “streetlight effect” of big data studies.

Photo by Riccardo Francesconi

In The Conversation, Mark Moritz, an associate professor of anthropology at Ohio State University, argues that big data studies may suffer from the classic “streetlight effect” — “the tendency of researchers to study what is easy to study.” But studying what’s easy to study is not necessarily representative of reality, a potential pitfall for many “big data” studies.

As an example, Mortiz offers the “WEIRD” problem found in many research studies:

Harvard professor Joseph Henrich and colleagues have shown that findings based on research conducted with undergraduates at American universities — whom they describe as “some of the most psychologically unusual people on Earth” — apply only to that population and cannot be used to make any claims about other human populations, including other Americans. Unlike the typical research subject in psychology studies, they argue, most people in the world are not from Western, Educated, Industrialized, Rich and Democratic societies, i.e., WEIRD.

That same principle may be true of of big data studies focusing on social media users, for example, who aren’t necessarily typical.

To avoid such “streetlighting” and related research pitfalls, Moritz argues that context is key:

Understanding the differences between the vast majority of humanity and that small subset of people whose activities are captured in big data sets is critical to correct analysis of the data … For data analytics to be useful, it needs to be theory- or problem-driven, not simply driven by data that is easily available. It should be more like ethnographic research, with data analysts getting out of their labs and engaging with the world they aim to understand.