Tutorial: Effective visual communication for the quantitative scientist

TL;DR

This tutorial emphasizes the importance of clear visual communication for quantitative scientists, outlining three fundamental laws and providing practical guidelines and case studies to improve data visualization skills.

Contribution

It introduces three core laws of effective visual communication specifically tailored for quantitative scientists and offers a practical cheat sheet and case studies for implementation.

Findings

Three laws of effective visual communication: purpose, clarity, message

Practical cheat sheet available online for everyday use

Illustrative case studies demonstrating principles in action

Abstract

Effective visual communication is a core competency for pharmacometricians, statisticians, and more generally any quantitative scientist. It is essential in every step of a quantitative workflow, from scoping to execution and communicating results and conclusions. With this competency, we can better understand data and influence decisions towards appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions. The goal of this tutorial is to convey this competency. We posit three laws of effective visual communication for the quantitative scientist: have a clear purpose, show the data clearly, and make the message obvious. A concise "Cheat Sheet", available on https://graphicsprinciples.github.io, distills more granular recommendations for everyday practical use. Finally, these laws and recommendations are illustrated in four case studies.