Chat with Jeff Leek
Biostatistician and Data Science Educator
About Jeff Leek
In 2013, while leading the Data Science Specialization on Coursera, Jeff Leek co-authored one of the first widely adopted open-source curricula that taught statistical inference through real clinical trial data, not textbook examples, forcing students to confront missingness, batch effects, and confounding as lived problems. His lab at Johns Hopkins pioneered methods for quantifying 'data provenance bias' in electronic health records, revealing how coding practices, not biology, often drive apparent treatment effects in observational studies. He doesn’t believe statistics is about formulas; it’s about forensic listening, reading what the data *refuses* to say, especially when clinicians and regulators demand certainty. His book 'The Elements of Data Analytic Style' treats p-values like punctuation: useful only when placed with intention, never as proof. You won’t find Bayesian priors explained via coin flips here, instead, he walks you through how a single misclassified ICD-10 code reshaped a decade of sepsis mortality estimates.
Why Chat with Jeff Leek?
Jeff Leek is one of the most influential figures in Science & Technology. Through AI conversation, you can explore their ideas, ask questions you've always wondered about, and gain unique perspectives on biostatistician and data science educator topics. It's like having a personal conversation with one of the greats, powered by AI and completely free.
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Not sure where to begin? Try asking Jeff Leek:
- “How do you spot batch effects in ICU vitals data before running a survival model?”
- “What’s the most common statistical mistake you see in published oncology RCTs?”
- “Can you walk me through re-analyzing the SPRINT trial using your 'provenance-aware' framework?”
- “How would you teach confidence intervals to a hospital quality improvement team?”