Oct. 7, 2026

If you check the statistical assumptions of your analysis and find that they’re violated in your particular situation, that means you’re fitting the wrong model to the data.

A slight or mild violation of the assumptions might not be anything to worry about. It might not change the results discernibly, or it might mean the results are only approximately correct.

It’s ultimately a question of what level of precision you need for your business situation.

• If you don’t care whether the conclusions from the data are even true, then you don’t need to check statistical assumptions at all!
• If a close approximation of the truth is sufficient for your business situation, then you might choose to permit mild violations.
• If you need to make high-precision predictions or base critical business decisions on the conclusions, then the assumptions are as important as the findings themselves.

Yep. This gets pretty subjective and nuanced.

The truth is that the assumptions are never perfectly met, because the assumptions describe purely theoretical models.

(It’s the same concept as how there’s no perfect circle in physical reality. But, like the abstract concept of circles, our statistical models are incredibly useful, when used appropriately.)

So it’s aways a question of how much of a violation of the assumptions you’re willing to permit.

This requires careful and experienced judgment. And unfortunately it’s an area where AI is (currently) pretty terrible at making judgments.

There are two ways to ensure these judgments are made correctly:

1. Get a lot of experience looking at both real-life data and simulated data.
2. Get someone stats-oriented on your team.