Sensitivity analyses are often presented as proof that a result is robust and trustworthy. They are supposed to show that findings hold up even when assumptions are changed. But what if the analysis only tested a tiny corner of the uncertainty that actually matters? 

In this episode, we break down why sensitivity analyses often fail, how local robustness can create false reassurance, and why truly strong evidence has to challenge deeper sources of fragility.

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