
ERIC J. DAZA
I help you learn about yourself with your own data.
Biostatistician + Health Data Scientist (20+ years)
Stats-of-1 Creator
Daza (2018): n-of-1 "Granger causal inference" via average period treatment effect (APTE)
ASA Justice, Equity, Diversity, and Inclusion (JEDI) Outreach Group Professional Development Chair
"Significance does not imply importance."
"Significance of evidence is not evidence of significance."
(photo by Adam Chapin Photography)
(Barong Tagalog by Nostalgia Barong at Saya; learn more about this national garment of the Philippines at Pineapple Industries)

Bio
"Empathize exactly, communicate concisely." I strive to advance the science of individual well-being by bridging and communicating statistical concepts with precision and empathy. I work to coordinate efforts to innovate, refine, and implement statistical study design and analysis practices that strengthen the scientific, regulatory, and ethical rigor of health promotion and disease prevention using digital tools and data.
To do so, I use my natural instincts to find connections across different practice areas, identify underlying concepts shared by distinct technical disciplines and philosophies, and highlight similarities among various personal histories and experiences. I communicate my findings through metaphor and wordplay punexpectedly often.
Dr. Eric J. Daza is a biostatistician and health data scientist at Evidation, a digital health company. He has worked for 20 years in both industry and academia as a biostatistician and data scientist, in pharma clinical trials, survey sampling, nutrition, maternal/child health, global/international health, health promotion & disease prevention, healthtech, digital health, and behavioral medicine. In 2022, he was recognized by Forbes as one of 16 Healthcare Innovators That You Should Know, and by Fortune as one of 10 innovators shaping the future of health. In 2023, he was recognized likewise by the American Statistical Association.
Daza introduced the average period treatment effect (APTE) n-of-1 counterfactual framework (Daza, 2018) for understanding each person’s own health causes and effects using their own self-tracked wearable, sensor, or app data, along with the MoTR ("motor") method (Daza and Schneider, in preparation) for estimating such individual-specific causal effects. He created and edits Stats-of-1, a health statistics blog on a mission "to facilitate cross-disciplinary collaboration that will enhance idiographic data collection and analysis procedures across health disciplines." Daza is also an early Expert Member of the International Collaborative Network for N-of-1 Clinical Trials and Single-Case Experimental Designs.
As a privileged middle-class Brown Asian immigrant, Eric J. earned both his BA in Neurobiology / Cognitive Studies and MPS in Applied Statistics at Cornell University, followed by his DrPH in Biostatistics at the University of North Carolina at Chapel Hill. He then trained as a postdoc at the Stanford Prevention Research Center. He is also Jesuit-trained.
Here's a fun video by Eric Jay's brilliantly witty UNC friends in which he plays a singing statistics professor: A Statistical New World (see Videos page for more clips)
"Uncertain times call for certain measurements." (Science, Apr 2017)
"Significance of evidence is not evidence of significance." (Towards Data Science, Jun 2021)
(photo by Monica Semergiu)
Manifesto
I'm a statistician.
Statistics isn't a set of tools and theories for me. It's an entire philosophy, a way of understanding the world, a fundamental framework that constantly delights and surprises me by revealing deep connections across fields and phenomena.
It is a way of life.
Please know that when I give you statistical feedback or criticism, it is from this deep place of respect for my field, for my calling. And it is also from my deep commitment to want you to succeed.
I'm trying to help: Not police you as a gatekeeper of knowledge and terminology, but help you change intuitive but bad analytic habits as a fellow player-coach.
Even I occasionally make these mistakes, despite my deep training. Human intuition and habit can be a very hard thing to recognize and change.
I don't always get it right, but I'm trying. Please tell me when I mess up, so I can change my own behavior in a way that better helps our whole team succeed. Thank you for your help and feedback.
(Thank you to Adrian Olszewski for recognizing that this missive was in essence a manifesto.)