> That a rather assertive statement. I dare say most distributions are (due to CLT) Gaussian.

I have been doing stats/ML/data-science for more than a decade. The above has rarely been true ever in the datasets that I have looked at. Unless of course the raw data has been transformed in ways to make them look Gaussian. Gaussian is the exception, not the rule.

Distributions are only assumed. There is no such thing as truly normal data or sampling distribution.

Sure, though that's a bit like saying there aren't any perfect squares only things you assume are squares.

There is a difference. Distributions are tested in practice, for example, by using a goodness-of-fit test. Often if the hypothesis of normality is not rejected, people think this implies normality.