Comment by _Nat_ | Hacker News Reader

Comment by _Nat_ | Hacker News Reader

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Inability.

One of the foundational problems with the soft/social sciences is that many of its practitioners have such weak analytical thinking skills (Note below) and the educational requirements for Psychology majors typically just include a few semesters of statistics courses designed to be understandable for them.

Peer reviewers in Psychology generally don't expect authors to go much beyond the basic statistical tricks students are taught in undergrad. From prior discussions with professors in these fields, it's my impression that most are unable to understand just how flimsy such methods are.

Personally, my prediction is that this won't get better until we have more AI-guided analytical analysis software packages that make more robust analysis accessible to those in soft-science fields. This is, something to replace reliance on stuff like R^2 and p- values.

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Note: Just to provide some numerical evidence for the assertion that Psychology practitioners tend to be weak with their analytical skills, here's [a PDF of GRE scores](https://www.ets.org/s/gre/pdf/gre_guide_table4.pdf). GRE test-takers planning to study "Psychology" had quantitative reasoning (QR) mean of 149. This is pretty bottom-of-the-barrel; even people planning to go for grad school to do "Arts - Performance & Studio" did a little better, with an average of 151.

"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...)

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.

I'll admit that might have something to do with it, although psychology GREs are misleading because a lot, probably the vast most majority, of those test-takers are hoping to go into clinical psych, especially to practice therapy. This leads to issues in a number of ways: (1) many of those applicants really don't understand what psychology is about, (2) most (90% in research-oriented programs) are rejected, and (3) among what's left, it has to be said that they probably don't need to be the most quantitative to be a good therapist, if they're not doing research.

Psychology is a strange science in that it's a mixture of people with very unquantitative backgrounds, and those who deal with very complex math and statistics. What many don't realize is that meta-analysis itself really was developed as a method in psychology (even if it technically has its origins earlier with Pearson). This registered replication work is an extension of that, again being done by psychologists. It's probably safe to say that more empirical and statistical research on the scientific process itself has been done by psychologists (along with statisticians and many public health researchers) than any other discipline.

In any event, replication problems happen in other domains as well. This has been documented empirically. It might be worse in the biomedical domain than, say, physics or chemistry, but it's not limited to psychologists. What I see in the neurosciences per se is just as bad, if not worse (because it's ignored more).

I think ignorance of issues pertaining to overfitting, etc. definitely contributes, but I also think that ignorance is pretty widespread, and the problems can be sort of pernicious in that they don't always operate intuitively.

The GRE is nonsense anyway so I wouldn't really take it as meaning much of anything. Just look at how awful the subject tests are to see why nobody should ever give GRE scores more than a passing glance.

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