#143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg

Learning Bayesian Statistics

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• Intro to Bayes Course (first 2 lessons free)
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Takeaways:

• Bayesian mindset in psychology: Why priors, model checking, and full uncertainty reporting make findings more honest and useful.
• Intermittent fasting & cognition: A Bayesian meta-analysis suggests effects are context- and age-dependent – and often small but meaningful.
• Framing matters: The way we frame dietary advice (focus, flexibility, timing) can shape adherence and perceived cognitive benefits.
• From cravings to choices: Appetite, craving, stress, and mood interact to influence eating and cognitive performance throughout the day.
• Define before you measure: Clear definitions (and DAGs to encode assumptions) reduce ambiguity and guide better study design.
• DAGs for causal thinking: Directed acyclic graphs help separate hypotheses from data pipelines and make causal claims auditable.
• Small effects, big implications: Well-estimated “small” effects can scale to public-health relevance when decisions repeat daily.
• Teaching by modeling: Helping students write models (not just run them) builds statistical thinking and scientific literacy.
• Bridging lab and life: Balancing careful experiments with real-world measurement is key to actionable health-psychology insights.
• Trust through transparency: Openly communicating assumptions, uncertainty, and limitations strengthens scientific credibility.

Chapters:

10:35 The Struggles of Bayesian Statistics in Psychology

22:30 Exploring Appetite and Cognitive Performance

29:45 Research Methodology and Causal Inference

36:36 Understanding Cravings and Definitions

39:02 Intermittent Fasting and Cognitive Performance

42:57 Practical Recommendations for Intermittent Fasting

49:40 Balancing Experimental Psychology and Statistical Modeling

55:00 Pressing Questions in Health Psychology

01:04:50 Future Directions in Research

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