The problem I see with guard rails is that it's very hard to know if you're doing statistics right, due to its nature.
Inference is sometimes hard enough on its own (and I sometimes use computational methods in addition to theory just to double-check my results, but that's just the innermost layer.
Outside of that you have to define appropriate and efficient samples, which is more difficult. You have to know what population you're actually interested in, which is less obvious than it sounds like, and on top of that you have to pick an experimental/observational method that minimises error and ideally lets you quantify it -- extremely hard in most practical cases.
Add to that the fact that the outcome of statistical analysis might often be, "well, we still don't know anything meaningful!" But if you say that, someone else will sound more confident and guess who people will listen to?
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The way out is not guard rails, it's much better training from earlier ages. This stuff is hard and we are not born with intuition for it. We need lots of practise.
I still don't get why there's so much analysis and calculus in our curricula -- those are problems we can solve with numerical (sometimes statistical) methods. We ought to replace at least half off that with more probability and statistical inference and experimental design.