Facts or a conclusion made from the survival group assuming that it was truth, yet the results are bias because they are collected only from the survivors, the dead cannot tell their story so are not included in the conclusion, but their views could tell what we don’t get from the survivors, and could have been the most deciding factors.

For example:  The planes came back shot up, and the armour plating was put in the places where the most bullets were found on the returned/survivors planes.
But the conclusions came from the planes that made it back to the airfield during a war so the bullets were not in places that caused the non-survivor’s planes to fall.
It would seem better to find out where the planes that were shot down and didn’t make it back were shot, because that is where the true damage was made that stopped the planes. This is where the armour should be.

Survivorship bias or survival bias is the logical error of concentrating on the people or things that made it past some selection process and overlooking those that did not, typically because of their lack of visibility. This can lead to some false conclusions in several different ways. It is a form of selection bias. – Wikipedia.

What I get from this is that we make conclusions from what we assume is the full story when in reality it is only the portion that is available to use or what we want to use to get our conclusions.
Being bias in any area or in any way while trying to find something out will lead to misleading results.

Survivorship bias is the act of focusing on successful people, businesses, or strategies and ignoring those that failed. For example, in WWII, allied forces studied planes that survived being shot to discern armor placement. By neglecting bullet holes on lost planes, they missed armoring planes’ most vulnerable areas. Also called “survivor bias,” this phenomenon refers to the human tendency to study successful outcomes and ignore the accompanying failures. Because of this, we adopt opinions, structure businesses, and make decisions without examining all the data, which can easily lead to failure. – Meg Prater.

All the best from
James M Sandbrook.
6th of November, 2021.