Survivor bias is ignoring the invisible failures in your correlation analysis. What we have in regards to health research is the opposite. Every single study I heard about is about (preventing) causes of death.
What you're saying is, essentially, that this guy's age should be ignored because his longevity is due to luck. But I haven't seen anything that rules out other possibilities.
> What you're saying is, essentially, that this guy's age should be ignored because his longevity is due to luck.
That is absolutely not what I was trying to say. Sorry for the confusion. I was responding to the "look, he's old! let's see what he did to get there," comment: it's a form of survivor bias.
It's not about this man, or his life choices. Rather, it's about us, looking to survivors for lessons on survival.
Wikipedia has a good page on it:
Survivorship bias, or survival bias, is the logical error of concentrating on the people or things that "survived" some process and inadvertently overlooking those that did not because of their lack of visibility. This can lead to false conclusions in several different ways. The survivors may be actual people, as in a medical study, or could be companies or research subjects or applicants for a job, or anything that must make it past some selection process to be considered further.
Like others said, beware selection bias.
On the other hand, when someone gets to 100 there is a big chance someone did not do something wrong too often. In other words, if you do wrong things (diet, etc) your potential chances to get to 100 will drop significantly.
(Did you know that the guy who invented LSD died at 102?)