I've been diving into some data entry analysis lately, and I'm encountering a bit of a roadblock. Specifically, I'm struggling with understanding the best practices for handling outliers and inconsistencies in the data.

Should we remove them outright, or is there a more nuanced approach that can preserve the integrity of the dataset while still addressing these issues? Additionally, I'm curious about the most effective methods for detecting errors in large datasets. Any insights, experiences, or recommended resources would be greatly appreciated!

Thanks in advance!