there are a lot of gotchas that don't seem like errors but completely invalidate analysis when done without a thorough understanding of a technique.

For example you can learn regression from a stats textbook but unless you've gone through a thorough (and painful) graduate-level stats course, you probably haven't seen the edge cases that invalidate assumptions and necessitate a more complex regression e.g. your regression may suggest there is no effect but when you look at the residuals, you may find systematic bias that you can model using a subject-specific random effect or some transformation as a generalized linear model...

That isn't to say you need a graduate level stats degree but applying statistics without understanding the pitfalls can lead to seriously wrong conclusions.