Survivorship Bias is a logical error that occurs when we focus on successful outcomes while ignoring those that did not survive or succeed. This can lead to overly optimistic conclusions because we only see the "survivors" and miss the full picture, including failures that could provide valuable insights. For example, during World War II, Abraham Wald, a statistician, analyzed damaged aircraft to determine where to add armor. He noticed that the bullet holes were concentrated in certain areas, but he recommended reinforcing the areas with no damage. This was because the undamaged areas indicated where planes were likely shot down, illustrating how ignoring failures can lead to misguided decisions.