Zoe LeBlanc asked how basic statistics lead to a meaningful historical argument. A good discussion followed, worth reading, but since I couldn’t fit my response into tweets, I hoped to add a bit to the thread here on the irregular. I’m addressing only one tiny corner of her question, in a way that is peculiar to my own still-forming approach to computational history; I hope it will be of some use to those starting out.
In brief, I argue that one good approach to computational history cycles between data summaries and focused hypothesis exploration, driven by historiographic knowledge, in service to finding and supporting historically interesting agendas. There’s a lot of good computational history that doesn’t do this, and a lot of bad computational history that does, but this may be a helpful rubric to follow.
Zoe’s question gets at the heart of one of the two most prominent failures of computational history in 2017 : the inability to go beyond descriptive statistics into historical argument. I’ve written before on one of the many reasons for this inability, but that’s not the subject of this post. This post covers some good practices in getting from statistics to arguments.
Historians, for the most part, aren’t experimentalists. Our goals vary, but they often include telling stories about the past that haven’t been told, by employing newly-discovered evidence, connecting events that seemed unrelated, or revisiting an old narrative with a fresh perspective.
Read the full post here.