Standard errors are one of the most overlooked pieces of statistical output. They sit underneath confidence intervals, p-values, and claims about precision in almost every study. But what if those standard errors are wrong from the start? 

In this episode, we break down what standard errors actually represent, why they often fail when real-world data violate model assumptions, and how this creates false confidence in research findings.

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