prediction vs. modelling
I am currently working on a super cool project and I was thinking about the following distinction: modelling of data vs. prediction with data. If you give data to a physical science or engineering type, then they want prediction. They want to come up with an accurate prediction of some future state. You want tiny errors. In contrast, most social scientists are interesting in modelling general trends. We understand that statistical models have error terms, so prediction is inherently hard. It’s even beside the point in some sense. If X perfectly predicts Y, you’ve probably just measured the same thing twice. Instead, you want an imperfect, but unexpected, relationship between variables. Neither approach is wrong, but they do represent different philosophies of data analysis.