DFW Data Science and Statistics Day 2026

The D³ (DFW Data Science and Statistics Day) poster competition provides an excellent platform for students and postdocs in data science, statistics/biostatistics, and business in the DFW area to present their research and connect with peers. This event welcomes both statisticians and applied scientists engaged in research with a significant quantitative focus. We encourage participation, whether as an attendee or presenter.

The D³ poster competition is open to undergraduate students, graduate students, and postdocs in data science, statistics/biostatistics, bioinformatics, and business at any U.S. higher education institution, especially for those in the DFW area such as SMU, TCU, UNT, UNTHSC, UT Arlington, UT Dallas, UT Southwestern, and UT Tyler). Cash prizes and honorable mentions will be given to the Best Poster awardees.

03/26/2026 (Thursday), 4:00 pm - 8:00 pm CT
Agenda (tentative)
3:30 - 4:00 pm: Check-in and opening remarks
4:00 - 5:30 pm: Poster session
5:30 - 6:00 pm: Social mix and light dinner
6:00 - 7:00 pm: Talk
7:00 - 8:00 pm: Poster award ceremony and introduction to the new officers

Abstract Submission:
The abstract submission deadline is March 19, 2026. To submit your abstract, please click this link.

Speaker:
Jeffrey S. Morris
 George S. Pepper Professor of Public Health and Preventive Medicine
 Professor and Director, Division of Biostatistics, DBEI
 Perelman School of Medicine, University of Pennsylvania
 Professor of Statistics and Data Science, The Wharton School.

Title:
Seeing Through Epidemiologic Fallacies: How Statistics Safeguards Scientific Communication in a Polarized Era

Abstract:
Observational data underpin many biomedical and public-health decisions, yet they are easy to misread, sometimes inadvertently, sometimes deliberately, especially in fast-moving, polarized environments during and after the pandemic. This talk uses concrete COVID-19 and vaccine-safety case studies to highlight foundational pitfalls: base-rate fallacy, Simpson’s paradox, post-hoc/time confounding, mismatched risk windows, differential follow-up, and biases driven by surveillance and health-care utilization.
Illustrative examples include:
1. Why a high share of hospitalized patients can be vaccinated even when vaccines remain highly effective.
2. Why higher crude death rates in some vaccinated cohorts do not imply vaccines cause deaths.
3. How policy shifts confound before/after claims (e.g., zero-COVID contexts such as Singapore), and how Hong Kong’s age-structured coverage can serve as a counterfactual lens to catch a glimpse of what might have occurred worldwide in 2021 if not for COVID-19 vaccines.
4. How misaligned case/control periods (e.g., a series of nine studies by RFK appointee David Geier) can manufacture spurious associations between vaccination and chronic disease.
5. Why apparent vaccine–cancer links can arise from screening patterns rather than biology.
I will outline a design-first, transparency-focused workflow for critical scientific evaluation, including careful confounder control, sensitivity analyses, and synthesis of the full literature rather than cherry-picked subsets, paired with plain-language strategies for communicating uncertainty and robustness to policymakers, media, and the public. I argue for greater engagement of statistical scientists and epidemiologists in high-stakes scientific communication.