"One of the foundational problems with the soft/social sciences is that many of its practitioners have such weak analytical thinking skills (Note below)" Andre

"One of the foundational problems with the soft/social sciences is that many of its practitioners have such weak analytical thinking skills (Note below)" Andrew Gelman argued that a fundamental problem with peer review is that it is done by peers. Peers whose background and skills are similar to those of the authors, and thus not likely to catch things that were missed. [When does peer review make no damn sense?](https://andrewgelman.com/2016/02/01/peer-review-make-no-damn-sense/ https://andrewgelman.com/2016/02/01/peer-review-make-no-damn...) I'm sure something could be gained via education. I don't think you necessarily have to be good at math to understand the concepts. But a lot of undergrad courses focus on hypothesis testing and p-values (despite those methods being condemned by the American Statistical Association), and encourage memorization of steps to do simple math, over understanding what any of it means. I think the ad-hoc approach of many machine-learning intros would do better. Maybe programming isn't any easier for a psychology student, but simply hammering the ideas, with short demos of pitfalls, may help. For "small n" problems like in psychology, psych students are much better off with a statistics background than machine learning. So what I'm advocating is a change in how these classes are taught. Of course, I've known plenty of students who program via copy and pasting code, and modifying it until it runs as necessary. The equivalent of memorizing steps math steps. So it will take more to solve the problem.