Regression analysis estimates the effect of one variable while holding others constant — its great power and its great danger. If a relevant variable is left out of the model, its influence gets wrongly attributed to the variables that are included (omitted-variable bias), producing confident but false conclusions. This is the technical form of both correlation-without-causation and, in the wild, Observed success hides its real causes — the missing variable is often the hidden advantage.