P-values are everywhere in research. They are treated as the standard for determining whether a result is real, meaningful, or worth acting on. But what if statistical significance is answering the wrong question? 

In this episode, we break down why p-values often fail when the real goal is causal inference. You will learn what a p-value actually measures, why it cannot establish causality, and how study design, confounding, and bias matter far more than a single threshold like 0.05. 

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