> I think you have stated it rather bluntly, Oops. I was just talking from experience, where I had some expectations of understanding given the level of expert

> I think you have stated it rather bluntly, Oops. I was just talking from experience, where I had some expectations of understanding given the level of expertise professed that weren't met in practice. I say that and I don't even have a PhD (or any other degree!!) in statistics or ML... > I noticed a lack of holistic knowledge when it came to (say) the hardware that actually execute that machine learning. Exactly, there are a lot of people I talk to who seem to use "black boxes", without any understand of how it works. If they can be more productive, why not? However, when one of their black boxes is broken, they often get stuck: as they can't even understand how it works in normal cases, understanding when it may stop working, or even more importantly, how to fix it then, is just not possible. I have found that to often be the case with mathematics and statistics. Sometimes it can be a problem, so I have derived some quick test questions to evaluate the person understanding. They are not Shibboleth: they are not hurtful by virtue of having different "levels" of answer that the person may give. Typical example for stats/ML: "Can you explain why the LLN happens?" Bonus points if the person just tells me the Normal distribution is defined by a Gaussian that is the Fourier transform of itself.