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#18 How to ask good Research Questions and encourage Open Science, with Daniel Lakens

#18 How to ask good Research Questions and encourage Open Science, with Daniel Lakens

FromLearning Bayesian Statistics


#18 How to ask good Research Questions and encourage Open Science, with Daniel Lakens

FromLearning Bayesian Statistics

ratings:
Length:
58 minutes
Released:
Jun 18, 2020
Format:
Podcast episode

Description

How do you design a good experimental study? How do you even know that you’re asking a good research question? Moreover, how can you align funding and publishing incentives with the principles of an open source science?
Let’s do another “big picture” episode to try and answer these questions! You know, these episodes that I want to do from time to time, with people who are not from the Bayesian world, to see what good practices there are out there. The first one, episode 15, was focused on programming and python, thanks to Michael Kennedy. 
In this one, you’ll meet Daniel Lakens. Daniel is an experimental psychologist at the Human-Technology Interaction group at Eindhoven University of Technology, in the Netherlands. He’s worked there since 2010, when he received his PhD in social psychology. 
His research focuses on how to design and interpret studies, applied meta-statistics, and reward structures in science. Daniel loves teaching about research methods and about how to ask good research questions. He even crafted free Coursera courses about these topics. 
A fervent advocate of open science, he prioritizes scholar articles review requests based on how much the articles adhere to Open Science principles. On his blog, he describes himself as ‘the 20% Statistician’. Why? Well, he’ll tell you in the episode…
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/) !
Links from the show:
Daniel's website: https://sites.google.com/site/lakens2/Home?authuser=0 http://daniellakens.blogspot.com/ https://github.com/Lakens https://twitter.com/lakens?ref_src=twsrc%5Etfw https://scholar.google.nl/citations?user=ZbqYyrsAAAAJ&hl=nl https://www.coursera.org/learn/statistical-inferences https://www.coursera.org/learn/improving-statistical-questions https://opennessinitiative.org/ https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/ (https://sites.google.com/site/lakens2/Home)
The 20% Statistician: http://daniellakens.blogspot.com/ (http://daniellakens.blogspot.com/)
Daniel on GitHub: https://github.com/Lakens (https://github.com/Lakens)
Daniel on Twitter: https://twitter.com/lakens (https://twitter.com/lakens)
Daniel on Google Scholar: https://scholar.google.nl/citations?user=ZbqYyrsAAAAJ&hl=nl (https://scholar.google.nl/citations?user=ZbqYyrsAAAAJ&hl=nl)
Coursera Course -- Improving your statistical inferences: https://www.coursera.org/learn/statistical-inferences (https://www.coursera.org/learn/statistical-inferences)
Coursera Course -- Improving Your Statistical Questions: https://www.coursera.org/learn/improving-statistical-questions (https://www.coursera.org/learn/improving-statistical-questions)
Peer Reviewers' Openness Initiative: https://opennessinitiative.org/ (https://opennessinitiative.org/)
The Scientific Paper Is Obsolete -- Here’s what’s next: https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/ (https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/)



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Released:
Jun 18, 2020
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