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"What Is Survivorship Bias?" Topic

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Personal logo The Virtual Armchair General Sponsoring Member of TMP07 Jul 2019 10:58 a.m. PST

Remarkable article at this link

Short but fascinating explanation of how statistics in WW II could be so misleading in regard to aircraft damages/losses, but which has application beyond as well.

Highly recommended!


Oberlindes Sol LIC Supporting Member of TMP07 Jul 2019 1:01 p.m. PST

You don't need a Ph.D. in mathematics or statistics to tell you to protect the pilot, engines, fuel, and bomb load.

Personal logo Extra Crispy Sponsoring Member of TMP07 Jul 2019 1:18 p.m. PST

My web security software did not like that link – proceed with caution.

Personal logo Editor in Chief Bill The Editor of TMP Fezian07 Jul 2019 3:01 p.m. PST

The video version:

Fire at Will08 Jul 2019 7:33 a.m. PST

One of the first lessons from my Stats Degree was understand the source of the data before analysing

hindsTMP Supporting Member of TMP08 Jul 2019 8:31 a.m. PST

In a somewhat similar vein, WW2 combat accounts are from survivors. I always wondered how much this distorted the history. MH

Personal logo deadhead Supporting Member of TMP08 Jul 2019 11:51 a.m. PST

A very old but worth telling story.

My favourite has always been WWI British Army introduces steel helmets in 1916. Overnight the incidence/prevalence of serious head injuries shoots up. Much debate in high circles about faulty design and even dropping them, until the penny drops.

Pre helmets they would have all been dead

Patrick R08 Jul 2019 3:35 p.m. PST

Same with interviewing people who have had much success in any endeavour, some are just plain lucky, it's better to talk to those who tried and came close.

Patrick R08 Jul 2019 11:50 p.m. PST

There are more interesting examples of getting your dataset wrong :

Back in the 1960's scientists sent teams to catalog animals in their environment, studying their behaviour and trying to figure out the equation for the best possible "natural balance" so that National Parks and other preserves could be managed rationally.

It went completely wrong, what looked like a perfect mix of animals went pear-shaped most of the time, despite attempts to keep the ratios they had figured out stable by adding and removing animals. In the end they realized that their assumption was wrong and that there is no natural balance, no ideal mix of x-moose, y-bears, z-gnats. Populations fluctuated and varied according to conditions. A few still cling to the idea, but the mainstream long abandoned those models in favour of better conservation methods.

Another is the uncanny ability of animals to sense their way home, especially over vast distances.

After some thorough research they found that the claims of pets having travelled hundreds or even thousands of miles to get back with their owners were greatly exaggerated. The average pet only traveled a few dozen miles at best, through semi-familiar conditions most of the time.

But the saddest part is that of all lost pets, the overwhelming majority are lost and never get even near their destination, either disappearing, presumed to have died along the way or were picked up by others. And even those who made it usually needed some help along the way with people noticing a stray animal and then notifying the owners or taking them for most of the distance to be reunited.

In fact most lost pets underperform against expectations, even if they have multiple points to base their return journey upon like familiarity, helpful people etc.

Sadly many pets are simply lost and never return. And we focus so much on those who make it back that we get a false image.

Dynaman878909 Jul 2019 11:23 a.m. PST

The worst of the lot is "X" causes "Y". Most famously for violent video games in the form of "X" violent offenders played video games. The only important statistic would be how many violent video game players "Z" are also violent offenders "X". (and it is a miniscule percentage)

Lion in the Stars10 Jul 2019 7:43 p.m. PST

This exact example was mentioned in my Statistics classes in 2010.

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