We appear to have a number of overlapping threads on imbalanced data. Here are just a few of the more highly-upvoted ones:
Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
When is unbalanced data really a problem in Machine Learning?
Does an unbalanced sample matter when doing logistic regression?
What is the root cause of the class imbalance problem?
Some of these threads themselves link to and summarise even more questions and answers on the topic (there are 950 questions tagged unbalanced-classes, many of them popular). Stephan Kolassa's heroic effort to summarise and pin down the problem is probably the most comprehensive question on the topic, but there are useful answers strewn across many of these threads.
We still keep getting new questions about this, though, and IMO they often seem to attract misleading/incorrect answers. It would be useful to have a canonical thread to point people to on this, but I don't know enough about the topic to judge which of these could be merged/closed as duplicates/deemed canonical, so I thought I'd ask here. How can we best simplify this situation?