The "given data" paradigm undermines both cultures

TL;DR

This paper critiques the traditional 'two cultures' view in statistics, advocating for a broader perspective that emphasizes the importance of understanding data processes before and after the black box for innovation.

Contribution

It challenges the conventional focus on the black box in statistical modeling, proposing a more comprehensive view that includes data understanding and process transparency.

Findings

Highlights limitations of the 'black box' perspective

Encourages exploration of data processes before and after modeling

Suggests broader statistical innovation beyond traditional cultures

Abstract

Breiman organizes "Statistical modeling: The two cultures" around a simple visual. Data, to the far right, are compelled into a "black box" with an arrow and then catapulted left by a second arrow, having been transformed into an output. Breiman then posits two interpretations of this visual as encapsulating a distinction between two cultures in statistics. The divide, he argues is about what happens in the "black box." In this comment, I argue for a broader perspective on statistics and, in doing so, elevate questions from "before" and "after" the box as fruitful areas for statistical innovation and practice.