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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
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli and Adam Tilmar Jakobsen.
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Transcript
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Learning Bayesian Statistics 
