BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt
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BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt
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About this listen
Today’s clip is from episode 133 of the podcast, with Sean Pinkney & Adrian Seyboldt.
The conversation delves into the concept of Zero-Sum Normal and its application in statistical modeling, particularly in hierarchical models.
Alex, Sean and Adrian discuss the implications of using zero-sum constraints, the challenges of incorporating new data points, and the importance of distinguishing between sample and population effects.
They also explore practical solutions for making predictions based on population parameters and the potential for developing tools to facilitate these processes.
Get the full discussion here.
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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.