Before you listen to this episode, can you quantify how useful you expect it to be? That's a prior! And "priors" is a word that gets used a lot in this discussion with <a href= "https://www.linkedin.com/in/michael-the-data-guy-kaminsky/" target="_blank" rel="noopener">Michael Kaminsky</a> as we try to demystify the world of Bayesian statistics. Luckily, you can just listen to the episode once and then update your expectation—no need to simulate listening to the show a few thousand times or crunch any numbers whatsoever. The most important takeaway is that you'll know you've achieved Bayesian clarity when you come to realize that human beings are naturally Bayesian, and the underlying principles behind Bayesian statistics are inherently intuitive. This episode's Measurement Bite from show sponsor <a href= "https://getrecast.com/geolift" target="_blank" rel= "noopener">Recast</a> is a brief explanation of statistical significance (and why shorthanding it is problematic…and why confidence intervals are generally more practically useful in business than p-values) from <a href= "https://www.linkedin.com/in/michael-the-data-guy-kaminsky/" target="_blank" rel="noopener">Michael Kaminsky</a>! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the <a href= "https://analyticshour.io/2025/11/25/285-our-prior-is-that-many-analysts-are-confounded-by-bayesian-statistics/" target="_blank" rel="noopener">show page</a>.

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#285: Our Prior Is That Many Analysts Are Confounded by Bayesian Statistics

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