Adjustment for baseline differences is one of the most common moves in health research and biostatistics. It is often treated as proof that two groups have been made more comparable and that bias has been reduced. But what if that adjustment is creating more confidence than the data actually deserve? 

In this episode, we break down why baseline adjustment often fails, how observed balance can hide deeper structural non-comparability, and why adjusting for differences is not the same as solving them.

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