The ladder of abstraction in statistical graphics

TL;DR

The paper introduces a strategy for improving the clarity of statistical graphics by progressively increasing their abstraction level, helping communicate complex data more effectively.

Contribution

It proposes a ladder of abstraction approach for graphical communication, starting from simple plots and embedding them into more general frameworks.

Findings

Enhanced clarity in statistical graphics through the ladder approach

Demonstrated with examples involving equations and income-voting data

Improved interpretability of complex data visualizations

Abstract

Graphical forms such as scatterplots, line plots, and histograms are so familiar that it can be easy to forget how abstract they are. As a result, we often produce graphs that are difficult to follow. We propose a strategy for graphical communication by climbing a ladder of abstraction (a term from linguistics that we borrow from Hayakawa, 1939), starting with simple plots of special cases and then at each step embedding a graph into a more general framework. We demonstrate with two examples, first graphing a set of equations related to a modeled trajectory and then graphing data from an analysis of income and voting.