One tells you how scattered your individual measurements really are. The other tells you how precisely you've pinned down the average. Mix them up and a chart can make wildly inconsistent data look almost perfectly consistent.
Both numbers get reported with a "±" sign, both get called "the error," and both shrink or grow depending on the situation — which is exactly why they get swapped for each other constantly. But standard deviation and standard erroranswer genuinely different questions. Standard deviation describes the raw data itself: how much do individual measurements typically differ from the mean? Standard error describes something else entirely: how precisely does the sample mean estimate the true population mean? Confusing the two doesn't just muddy terminology — it can make a study or a test result look far more reliable than the underlying data actually is.