There's a famous saying in the world of data science.
All models are wrong but some are useful. — George E. P. Box
It may not only be data science either. But it doesn't matter.
When you're exploring a dataset, you're looking for clues. Sometimes they'll be right there, other times they won't.
You'll build models and they won't work. But you keep going.
You keep exploring, you keep looking for answers and you keep asking questions.
You prove yourself wrong over and over. And that's what you practice. You practice being wrong. You develop the most important skill a data scientist can have. A willingness to be wrong.
Not because the goal is to be wrong. But because being wrong gives you an opportunity to figure out what not to do.
Being wrong means you tried something which might not work.
Being wrong is the badge of the explorer.
Because being wrong and learning from it enables you to get closer to being useful.