HeyJaredSERious EPI
SERious EPI is a podcast hosted by Hailey Banack and Matt Fox where leading epidemiology researchers are interviewed on cutting edge and novel methods. Interviews focus on why these methods are so important, what problems they solve, and how they are currently being used.
- Indexed episodes, last 90 days
- 3
- Latest publication
- Sep 15, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 15, 2026
Latest episodes
S5E9: Random Error Is the Easy Part (opens the original)
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In this episode of SERious Epidemiology, Hailey and Matt are joined by Lucy D’Agostino McGowan to discuss Chapter 10 of Causal Inference: What If? and the problem of random variability. This episode explores the distinction between random error and systematic bias, what confidence intervals and standard errors actually tell us, and why increasing sample size can improve precision without making a biased estimate any closer to the truth. Lucy offers a statistician’s perspective on why random erro
S5E8: Selection Bias: Colliders, Censoring, and Confusion (opens the original)
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In this episode of SERious Epidemiology , Hailey and Matt are joined by Dr. Louisa Smith to discuss Chapter 8 of Causal Inference: What If on selection bias. The conversation explores how selection bias can arise through conditioning on a collider, how it differs from confounding, and how loss to follow-up and censoring can introduce bias even in randomized trials. A major theme of this episode is the relationship between selection bias and confounding. We also discuss generalizability, target p
S5E7: Exchangeability, Positivity, Consistency… and Measurement? (opens the original)
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In this episode, we talk with Dr. Alexis Reeves about Chapter 9 of Causal Inference: What If , focusing on measurement bias (the bias formerly known as information bias). Measurement bias arises when exposures, outcomes, confounders, or colliders are measured incorrectly. We discuss different types of measurement bias: differential, nondifferential, dependent, and independent, and errors in measurement of continuous variables vs. categorical variables. We follow the structure of the chapter, nex
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