Oct. 5, 2026

It’s no secret that in many applied data analytics contexts, formal steps like checking model assumptions are sometimes skipped.

If this is admitted with a blush indicating the appropriate level of embarrassment, that’s one thing.

It’s not great, but at least they’re aware that they have no idea if their findings are correct (or even interpretable).

What’s more concerning is when the statement is delivered confidently, as though to suggest that such stuffy procedures are not really necessary “in the real world.”

If that’s your stance, I have terrible news… You have no idea if your findings are correct (or even interpretable).

What are statistical assumptions?

They are NOT things we assume to be true.

They’re more like conditionals, as in, “If these conditions are true, then these results and conclusions will be correct.”

This means you can’t escape or ignore them. They’re part of the description of the model that you fit to the data.

And there are no escape hatches like large datasets or AI. I’m not aware of any statistical procedure that makes zero assumptions—not even analyses for big data or machine learning algorithms.