ER2: Ethical and Effective Practices for Statistical Graphics

Northeastern University, Boston MA

Investigators

Abstract

Part of being a good scientist is not just discovering new things but also communicating those findings to others. Scientists are taught how to navigate ethical issues around consent, honesty, and transparency in the conduct of research. The investigators of this project aim to expand this educational framework to also include teaching of how to be ethical statistical communicators, especially for the charts and graphs that are often the first or only contact that audiences have with scientific data. The wrong graph can hide important information, mislead its audience, or even dehumanize its subjects. Therefore, this project seeks to create a guide, for use by scientists from all STEM disciplines, for ethical statistical graphics: to create charts that are not just effective at showing data, but that transparently, honestly, robustly, and, above all, mindfully communicate information. The project also provides opportunities for graduate students to participate in the development and testing of guidelines for ethical and effective practices for statistical graphics. This project focuses on the development of a guide for ethical and effective practices for statistical graphics. The guide will be formed through a combination of interviews with existing practitioners and ethicists, and it will be validated through a set of laboratory studies. The investigators will structure the ethical principles that will make up the core of the guide through the lens of virtue ethics: ways that scientists can embody a set of proposed virtues in their work. The investigators intend on both creating guidelines based on a survey of prior work but also conducting their own crowdsourced experiments to empirically validate these guidelines, especially in places in which there are conflicts in values or virtues. For instance, the increased transparency that comes from including detailed statistical uncertainty information incurs a cost in complexity, and it limits the intended audience to those that understand how this uncertainty information was modeled: how do varying levels of uncertainty impact perceptions of scientific work, and is there a “sweet spot” between including too much uncertainty that can overwhelm the intended viewer, and too little that can mislead viewers or sweep potential issues around the strength or reliability of scientific findings under the rug? Once the investigators’ guidelines have been collected and the strength of the evidence assessed, the investigators will deploy the resulting guide for ethical statistical graphics in classrooms and workshops as well as online for use in training the next generation of scientists. It will also be used to inspire existing scientists and science communicators to think more deeply about the ethical issues underlying their work. This project is jointly funded through the ER2 program by the Directorate for Biological Sciences and the Directorate for Social, Behavioral and Economic Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.