In your experience, where do statistical models and standard inferential frameworks reliably predict outcomes—and where do they begin to break down in practice?

What conditions typically precede that breakdown (e.g., model misspecification, unobserved variables, overfitting, etc.)?

Does the failure appear gradually, or does performance seem stable until it suddenly isn’t?

After breakdown, do models become unpredictable—or do they continue producing consistent but misleading results?

Have you observed situations where models are revised, assumptions updated, or additional data incorporated—yet the overall conclusions or outcomes remain largely unchanged?

What mechanisms allow this stability (e.g., model flexibility, researcher degrees of freedom, institutional norms)?

Are these outcomes being actively maintained, or do they persist passively through the structure of the process?

Does increased rigor tend to correct these issues—or can it reinforce the same result?

When models no longer adequately explain or predict outcomes, what still determines the results in practice?

Are there underlying constraints (data limitations, structural dependencies, incentives, etc.) that continue to shape outcomes regardless of model accuracy?

Does the system require correct modeling to function, or does it proceed independently of whether the model is right?

In your view, what is most often mistaken for “control” or “understanding” that doesn’t actually determine the outcome?