By this I mean that to make confident predictions, you need some serious statistics, but psych is one of the least math heavy sciences (thankfully they recently learned about Bayes and there's a revolution going on). Unlike physics or chemistry, you have so little control over your experiments.
There's also the problem of measurements. We stress in experimental physics that you can only measure things by proxy. This is like you measure distance by using a ruler, and you're not really measuring "a meter" but the ruler's approximation of a meter. This is why we care so much about calibration and uncertainty, making multiple measurements with different measuring devices (gets stats on that class of device) and from different measuring techniques (e.g. ruler, laser range finder, etc). But psych? What the fuck does it even mean "to measure attention"?! It's hard enough dealing with the fact that "a meter" is "a construct" but in psych your concepts are much less well defined (i.e. higher uncertainty). And then everything is just empirical?! No causal system even (barely) attempted?! (In case you've ever wondered, this is a glimpse of why physicists struggle in ML. Not because the work, but accepting the results. See also Dyson and von Neumann's Elephant)
I've jokingly likened psych to alchemy, meaning proto-chemistry -- chemistry prior to the atomic model (chemistry is "the study of electrons") -- or to astrology (astronomy pre-Kepler, not astrology we see today). I do think that's where the field is at, because there is no fundamental laws. That doesn't mean it isn't useful. Copernicus, Brahe, Galileo (same time as Kepler; they fought), and many others did amazing work and are essential figures to astronomy and astrophysics today. But psych is in an interesting boat. There are many tools at their disposal that could really help them make major strides towards determining these "laws". But it'll take a serious revolution and some major push to have some extremely tough math chops to get there. It likely won't come from ML (who suffers similar issues of rigor), but maybe from neuroscience or plain old stats (econ surprisingly contributes, more to sociology though). My worry is that the slop has too much momentum and that criticism will be dismissed because it is viewed as saying that the researchers are lazy, dumb, or incompetent rather than the monumental difficulties that are natural to the field (though both may be true, and one can cause the other). But I do hope to see it. Especially as someone in ML. We can really see the need to pin down these concepts such as cognition, consciousness, intelligence, reasoning, emotions, desire, thinking, will, and so on. These are not remotely easy problems to solve. But it is easy to convince yourself that you do understand, as long as you stop asking why after a certain point.
And I do hope these conversations continue. Light is the best disinfectant. Science is about seeking truth, not answers. That often requires a lot of nuance, unfortunately. I know it will cause some to distrust science more, but I have the feeling they were already looking for reasons to.