I don't mean it to be cynical, it's just my experience that a lot of data gathering in the social sciences (my experience was in education) makes terrible assumptions, often implicitly, the worst being that the variables are IID. And it's very obvious from the publication that the number crunching is some cargo-cult approach they probably learned from their mandated semester or two in research methods. (Okay, that last bit was cynical!)
I could probably be characterized as a social scientist, at least a behavioral scientist.
What you're saying is probably part of it, although in my experience that criticism can be leveled as much, if not more, at wet-lab-type biologists who eschew all but the most minimal stats.
With the social sciences, though, there's another phenomenon at play, which is that the phenomena are so abstract often that there's not really a good theoretical reason to assume anything in particular. And if that's the case, because the normal is the entropy-maximizing distribution, you're actually better off assuming that rather than some other distribution. You could also use nonparametric stats, but that has its own advantages and disadvantages.
Bias-variance dilemma and all that.
The truth is, it's hard to beat the normal even when it's wrong. And if you subscribe to the inferential philosophy that every model is wrong, you're better off being conservatively wrong, which implies a normal.
I'm not saying everything should be assumed to be normal. But unless things are (1) obviously super non-normal, or (2) you have some very strongly justified model that produces a non-normal distribution, you're probably best off using a normal if you're going to go parametric. And I think those two conditions are met much more often than we like to admit.
The normal distribution is kind of over-maligned, I think. I started my stats career being enamoured of rigorously nonparametric stats, and still am (esp. exact tests, bootstrapping/permutation-based inference, and empirical likelihood), but have grown to strongly appreciate normal distributions (or whatever maxent distribution is appropriate).
From my perspective, either you have data and you have an idea what sort of distributions you're working with, or you don't, and you should fix that problem first rather than going down the theory rabbit hole.
With data in hand, a skew/kurtosis scatter plot is a good way to gauge the higher dimensional distribution of your data. Another option is to cluster the variables of the data set using something like HDBSCAN and color the plot points based on cluster membership.
If you have to go guessing distributions without evidence, you're better off choosing a low k student's T distribution (for robustness to outliers) or a gamma distribution (if you think your data might be skewed).
