Survivor bias is a form of selection bias

Selection bias is when you are doing a statistical analysis but you don’t get a good random sample because of your poor selection criteria

During World War II, the statistician Abraham Wald took survivorship bias into his calculations when considering how to minimize bomber losses to enemy fire.[10] Researchers from the Center for Naval Analyses had conducted a study of the damage done to aircraft that had returned from missions, and had recommended that armor be added to the areas that showed the most damage.[citation needed] Wald noted that the study only considered the aircraft that had survived their missions; any bombers which had been shot down or otherwise lost had logically also been rendered unavailable for assessment. The holes in the returning aircraft, then, represented areas where a bomber could take damage and still return home safely. Wald proposed that the Navy reinforce areas where the returning aircraft were unscathed[10]:88, since those were the areas that, if hit, would cause the plane to be lost. His work is considered seminal in the then-nascent discipline of operational research.[11]