There is a difference between effect size and significance. Things can be extremely statistically significant and have very small effect sizes. But the problem

There is a difference between effect size and significance. Things can be extremely statistically significant and have very small effect sizes. But the problem is less here and more that people don't care to understand the models they are using to judge statistical significance. A p-value is simply a magic wand to wave over the data and bless it. Statisticians may tend to look at the data a lot more qualitatively - a p-value might tell you something, but much more important is: "How accurately have I managed to model this system?" This is the larger problem lurking behind "p-hacking" and other colossal statistical fuck-ups: people don't understand the mathematical models they are applying, the limitations of the data, and often don't care to, as long as the veneer of having 'done something' can be applied. This, again, is probably a product of people cranking out shitty papers to make sure that they keep publishing, to continue eking out grants; which, again, is probably a product of research science being generally underfunded for the demands placed on it.