
TL;DR
This paper argues that in statistical practice, the choice of priors is crucial, demonstrating that many commonly used priors are incorrect and can lead to misleading results, through illustrative examples.
Contribution
It highlights the importance of selecting appropriate priors and shows that many standard priors are fundamentally wrong, impacting statistical inference.
Findings
Many standard priors are incorrect and lead to misleading inferences
Illustrative examples demonstrate the prevalence of wrong priors
Choosing the right prior is essential for valid statistical analysis
Abstract
All priors are not created equal. There are right and there are wrong priors. That is the main conclusion of this contribution. I use, a cooked-up example designed to create drama, and a typical textbook example to show the pervasiveness of wrong priors in standard statistical practice.
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