Statistical significance is one of the most familiar ideas in research. It is often treated as the dividing line between real evidence and random noise. But what if that binary framing is doing more harm than good?
In this episode, we break down why statistical significance often misleads, how threshold thinking distorts interpretation, and why effect size, uncertainty, and design quality matter far more than a simple significant versus non-significant label.
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