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#61 Why we still use non-Bayesian methods, with EJ Wagenmakers

#61 Why we still use non-Bayesian methods, with EJ Wagenmakers

FromLearning Bayesian Statistics


#61 Why we still use non-Bayesian methods, with EJ Wagenmakers

FromLearning Bayesian Statistics

ratings:
Length:
77 minutes
Released:
May 19, 2022
Format:
Podcast episode

Description

The big problems with classic hypothesis testing are well-known. And yet, a huge majority of statistical analyses are still conducted this way. Why is it? Why are things so hard to change? Can you even do (and should you do) hypothesis testing in the Bayesian framework?
I guess if you wanted to name this episode in a very Marvelian way, it would be “Bayes factors against the p-values of madness” — but we won’t do that, it wouldn’t be appropriate, would it?
Anyways, in this episode, I’ll talk about all these very light and consensual topics with Eric-Jan Wagenmakers, a professor at the Psychological Methods Unit of the University of Amsterdam.
For almost two decades, EJ has staunchly advocated the use of Bayesian inference in psychology. In order to lower the bar for the adoption of Bayesian methods, he is coordinating the development of JASP, an open-source software program that allows practitioners to conduct state-of-the-art Bayesian analyses with their mouse — the one from the computer, not the one from Disney.
EJ has also written a children’s book on Bayesian inference with the title “Bayesian thinking for toddlers”. Rumor has it that he is also working on a multi-volume series for adults — but shhh, that’s a secret!
EJ’s lab publishes regularly on a host of Bayesian topics, so check out his website, particularly when you are interested in Bayesian hypothesis testing. The same goes for his blog by the way, “BayesianSpectacles”.
Wait, what’s that? EJ is telling me that he plays chess, squash, and that, most importantly, he enjoys watching arm wrestling videos on YouTube — yet another proof that, yes, you can find everything on YouTube.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) !
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, 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, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland and Aubrey Clayton.
Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
Links from the show:
EJ’s website: http://ejwagenmakers.com/ (http://ejwagenmakers.com/)
EJ on Twitter: https://twitter.com/EJWagenmakers (https://twitter.com/EJWagenmakers)
“Bayesian Cognitive Modeling” book website: https://bayesmodels.com/ (https://bayesmodels.com/)
Port of “Bayesian Cognitive Modeling” to PyMC: https://github.com/pymc-devs/pymc-resources/tree/main/BCM (https://github.com/pymc-devs/pymc-resources/tree/main/BCM)
EJ’s blog: http://www.bayesianspectacles.org/ (http://www.bayesianspectacles.org/)
JASP software website: https://jasp-stats.org/ (https://jasp-stats.org/)
Bayesian Thinking for Toddlers: https://psyarxiv.com/w5vbp/ (https://psyarxiv.com/w5vbp/)
LBS #31, Bayesian Cognitive Modeling & Decision-Making with Michael Lee: https://www.learnbayesstats.com/episode/31-bayesian-cognitive-modeling-michael-lee...
Released:
May 19, 2022
Format:
Podcast episode

Titles in the series (100)

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Paris. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy