Creating Shared Understanding in Statistics and Data Science Collaborations

Abstract

Statisticians and data scientists have been called upon to increase the impact they have through their collaborative projects. Statistics and data science practitioners and their educators can achieve and enable greater impact by learning how to create shared understanding with their collaborators as well as teaching this concept to their students, colleagues, and mentees. In this article, we explore and explain the concepts of common knowledge and shared understanding, which is the basis for action to accomplish greater impacts. We also explore related concepts of misunderstanding and doubtful understanding. We describe a process for teaching oneself and others how to create shared understanding. We conclude that incorporating the concept of shared understanding into one’s practice of statistics or data science and following the steps described will result in having more impact on projects and throughout one’s career.

Keywords:

• Common knowledge
• Communication
• Consulting
• Doubtful understanding
• Statistical practice
• Statistics education

Acknowledgments

The authors thank all of the students, mentees, workshop participants, and domain experts we have worked with over the years as well as our colleagues and mentors who have helped us improve our collaboration skills and methods for teaching and assessing them. We also thank Marina Vance for her artwork.

Additional information

Funding

This work was supported by the National Science Foundation under Grant No. 1955109, Grant No. 2022138, and Grant No. 2044384 for the projects, “IGE: Transforming the Education and Training of Interdisciplinary Data Scientists (TETRIDS)”, “NRT-HDR: Integrated Data Science (Int dS): Teams for Advancing Bioscience Discovery”, and “CODE:SWITCH: Integrating Content and Skills from the Humanities into Data Science Education.” This work was also supported by the United States Agency for International Development under Cooperative Agreement Number 7200AA18CA00022 for the project, “LISA 2020: Creating Institutional Statistical Analysis and Data Science Capacity to Transform Evidence to Action.”