How do I learn when a statistical model breaks or gives wrong answers?

You can fit a basic model and get a result. But you sense the gap: you do not really know what the model assumes, or the conditions under which it quietly falls apart. That unease is honest and useful. A model you do not understand is a tool you cannot fully trust, and part of you knows it. Every model is a set of assumptions about how the world behaves. When those assumptions match reality, the answer is sound. When they do not, the model still hands you a number, confident and wrong. Learning a method is not just learning how to run it. It is learning where it lives and where it dies. So for each model you use, learn three things on purpose: what it assumes, how to check whether those assumptions hold, and what tends to break it. Read one clear explanation, then test it on data where you already know the answer and watch how it behaves. Understanding grows fastest when you deliberately push a tool until it fails. Once you have seen it break, you will never blindly trust its output again, and that is exactly the skill you are after.