30 Sep 2021

Eric J. Daza - StoRies from Biostatistics to Health Data Science

This talk surveys various R-related activities in biostatistics and health data science.

14 Sep 2021

Data Science in the Biomedical Industry

This career symposium is designed to offer attendees the opportunity to hear from diverse and experienced data scientists about their education and career paths, the skills expected of these positions, where and how to seek these types of positions, and what to expect when working in these fields.

26 Aug 2021

Statistical considerations for successful digital health innovation

Why should you report your modeling plan or statistical analysis plan before seeing any data? Why should we all ditch the term ‘statistical significance’ but keep statistical evidence? And how? A fantastic discussion with Eric Daza, Lead Statistician for Digital Health Outcomes at Evidation Health, as he dives into key themes from his recent pieces: Artifice or intelligence? and Ditch ‘statistical significance’.

14 Jun 2021

Eric Daza | N-of-1 Science & Causal Inference | Philosophy of Data Science

Much of our scientific inference revolves around the identification and replication of patterns in data. So what can be done when N=1? Eric Daza gives us a statistician's perspective on the ideas behind N-of-1 studies, its best examples, and strongest critiques.

30 Jun 2020

FilipinxAms in Healthtech + Safety During COVID19

Charity will discuss her background as a health and safety expert, the Return to Work Guidelines and her current work with various tech companies on re-opening during COVID19 and its impact on communities and communities of color.

Eric will discuss his professional background in healthtech and technical and creative contributions in STEAM in general and also by communities of color during COVID19.

11 Oct 2017

Design Trumps Analysis: Drawing Causal Conclusions using Big Data

This talk provides a high-level, fairly non-technical introduction to causal discovery using big data; i.e., how to carefully draw causal conclusions from big data analyses. Two general, complementary approaches for causal discovery will briefly be illustrated in the context of big data analysis: 1.) mechanism-focused and structural approaches using causal graphs, and 2.) the effect-focused statistical framework of potential outcomes (emphasis on the latter).

15 Mar 2017

Three Statistically Significant Principles

This is a short presentation I gave at the Quantified Self Bay Area Meetup event titled "Show & Tell #41" on March 15, 2017.  Summary:

  1. Big data does not imply big accuracy. (S)

  2. Significance does not imply importance. (S)

  3. Correlation does not imply causation. (P)

  4. Causation can imply correlation. (P)

Aug 2011

A Statistical New World

Some excellent grad school friends of mine created this fun musical take (based on Disney's "A Whole New World") on what it's like to be a statistician---and asked me to perform and handle music production! From the 2011 American Statistical Association "Promoting the Practice and Profession of Statistics" Video Competition.