Anecdotally, I'd say I fail most things 95% or more of the attempts. That's why we rewrite, debug, practice, drill, google, avoiding failure takes a lot of human effort.
My hypothesis is that we model the system we're studying and simulate many 'attempts' for every real world attempt. I.e. we grow a low-fidelity, but much faster, model of the system in our brain that we can use to make medium-low confidence predictions about the real system many times for each time we test against the real system.
So when you say you fail 95% of the time, I'm saying each of those failures actually have 200 mini-successes embedded that you can still use to train your mental model.
Sometimes people go into a situation confused, fail, and don't know how to interpret why they failed or what parts caused the failure. I think it is worth distinguishing that type of failure from the type you're talking about. Why? Often when you tell someone you don't know how to do something or you think you'll fail at something, they have your type of failure in mind and they encourage you to just try again.
It seems necessary to include a model of self to reach the kind of predictive ability necessary to learn from few examples.
Once burned, twice shy. And often that results in irrational aversion to huge classes of behaviors just because they appeared in the larger context of the failure of an endeavor as a whole, which I'd say is not a good way to learn from failures.
If you failed to succeed in the expected amount of time/effort, it's a failure. Maybe you add some tolerance of going past the estimate before classifying as a failure, but it's still rooted in the expectation.
For example, if it took you ten years to pass kindergarten, you've failed.