I am increasingly worried with people applying ML in everything without any rigour. Statical inference generally only works well in very specific conditions: 1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong) 2 - Using (1), you calculate how much data you need so you get an estimation error below x% Even though most ML models are essentially…
i think this actually gets at what makes applied ML distinct from statistics as a practice, even though there is a ton of overlap. statisticians make assumptions 1 and 2, and think of themselves as trying to find the "correct" parameters of their model. people doing applied ML typically assume they don't know 1 (although they might implicitly make some weak assumptions like sub-gaussian to avoid fat tails, etc.) and…
Despite using similar models, the expertise required for 'doing statistics' (statistical inference) is actually very different from machine learning. Machine learning fits into the 'hacker mentality' well - try stuff out see what works. To do statistical inference effectively, you really do need to spend time learning the theory. They both require deep skills - but the skills are surprisingly different considering it's often the same underlying model.