‘Statistics at a Crossroads’
Recommendations Are Released

An NSF-sponsored report detailing a vision for the evolution of statistics in light of the growth of data science is now available to the public. Statistics at a Crossroads: Who Is for the Challenge? was written by a broad and diverse group of statisticians and organized by participants from a three-day workshop and two webinars on the topic last year.

The report makes recommendations in the following six areas:

  1. Placing practice at the center of statistics, with support from theory and computation
  2. Emphasizing the scientific and societal impact of statistical research, rather than publication quantity
  3. Formulating foundational research to reflect modern data problems
  4. Embracing the big research questions and grand challenges around statistical investigation itself
  5. Evaluating methodologies using broader metrics than just narrow optimality
  6. Training the next generation of statisticians and data scientists with modern skills of critical thinking, modeling, computation, and communication

The project was planned by a steering committee that included James Berger, Xuming He, David Madigan, Susan Murphy, Jon Wellner, and Bin Yu. Ten theme leaders—David Banks, Alicia Carriquiry, David Higdon, Jennifer Hill, Nicholas Horton, Michael Jordan, Marianthi Markatou, Dylan Small, Marina Vannucci, and Ming Yuan—moderated and summarized discussions from the workshop and webinars. The executive summary was prepared by He, Madigan, Wellner, and Yu.

This report was designed to be accessible to a wide audience of key stakeholders in statistics and data science, including academic departments, university administration, and funding agencies.

Read an excerpt from the executive summary below or view the full report.

The field of Statistics is at a crossroads: we either flourish by embracing and leading Data Science or we decline and become irrelevant. In the long run, to thrive, we must redefine, broaden, and transform the field of Statistics. We must evolve and grow to be the transdisciplinary science that collects and extracts useful information from data. With the fast establishment of various Data Science entities across campuses, industry, and government, there is a limited time window of opportunity for a successful transformation that we must not miss. We must effect this change now by reimagining our educational programs, rethinking faculty hiring and promotion, and accelerating the cultural change that is required.