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I help you use your own data to learn about yourself.

​Eric J. Daza has been a health data scientist for 21+ years (Cornell BA neurobiology, Cornell MPS applied statistics, UNC Chapel Hill DrPH biostatistics, Stanford postdoc). Daza created Stats-of-1, a health innovation newsletter/podcast (featured in Forbes and Fortune) on n-of-1 trials, single-case designs, switchback experiments, and personal AI for digital health/medicine. He invented a patent-pending method using his n-of-1 time series causal inference framework. Daza is a DEI leader at the American Statistical Association, Filipino American immigrant, and trained musician.

photo by Adam Chapin Photography

Barong Tagalog by Nostalgia Barong at Saya; learn more about this national garment of the Philippines at Pineapple Industries

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A Statistical New World
Meet Dr. Eric J. Daza: Biostatistician at Evidation Health
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Three Statistical Significant Principles by Eric Daza
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Data Science Philippines - Design Trumps Analysis: Drawing Causal Conclusions using Big Data
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Big Data Principles by Eric J. Daza
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Dr. Eric J. Daza (pronouns: he/him/siya) created Stats-of-1, a digital health newsletter for promoting the expanded use of n-of-1 trials, single-case designs, switchback experiments, and other individual-focused (personalized/precision) statistical approaches—for health and medicine in particular. These approaches can truly personalize health insights, diagnosis, and treatment in a way traditional clinical and biomedical statistics—and even many precision medicine, machine learning, and AI approaches—fundamentally cannot. For this innovative work, he was recognized by Forbes Magazine and Fortune Magazine in 2022, and by the American Statistical Association in 2023. He is also a General Member of the International Collaborative Network for N-of-1 Clinical Trials and Single-Case Experimental Designs.

Daza is a biostatistician and health data scientist. He has worked for 20+ years in industry and academia, in pharma clinical trials, survey sampling, nutrition, maternal/child health, global/international health, health promotion and disease prevention, healthtech, digital health, and behavioral medicine.


​​Since 2022, Daza has served as the Professional Development Committee (PDC) Chairperson for the Justice, Equity, Diversity, and Inclusion (JEDI) Outreach Group at the American Statistical Association. He has been an ASA JEDI PDC Member since 2021.

As a privileged middle-class Brown Asian immigrant from the Philippines, and as a neurodivergent person with ADHD, Daza 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.


Daza introduced the average period treatment effect (APTE) n-of-1 counterfactual framework for understanding each person’s own health causes and effects using their own self-tracked wearable/sensor data, patient-reported outcomes, clinical outcomes, and biomarkers (Daza, 2018; clearer LaTeX notation here). To do so, he introduced the MoTR ("motor") method for estimating such individual-specific causal effects (Daza and Schneider, in preparation; 2-min explainer video here).​

The APTE framework for "Granger causal inference" is grounded in n-of-1 trials and single-case designs. It accommodates both experimental (RCTs, A/B tests, experimentation) and observational (real-world) partitioned multivariate time series, heterogeneous treatment effects, functional data analysis, micro-randomized trials, dynamic treatment regimes, and multilevel/hierarchical/ mixed-effects models.



Here's a fun video by Daza's brilliantly witty UNC friends in which he plays a singing statistics professor: A Statistical New World (see Videos page for more clips)



(photo by Monica Semergiu)

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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.)

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San Francisco Bay Area, CA, USA

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