Survivorship Bias

Survivorship bias is a tricky fal­lacy to deal with.

We’re all mes­mer­ised by suc­cess stor­ies. But study­ing those who are suc­cess­ful isn’t always the best idea.

You might suc­cumb to sur­viv­or­ship bias.
You might bet on flawed strategies.

Here we go:

Abraham Wald and the WWII Planes

During World War II, the Allies stud­ied Nazi dam­age to their fight­er planes. Their study res­ul­ted in this dot­ted illustration:

The ana­lys­is of these dam­ages res­ul­ted in the idea that the Allied Forces should rein­force their fight­er planes in the areas show­ing the densest clusters of red dots. 

A stat­ist­i­cian by the name of Abraham Wald dis­agreed. Instead, he sug­ges­ted that they rein­force sur­faces with no red dots. Wald was right, of course. The red-dot­ted areas indic­ated non-fatal dam­aged regions by study­ing planes that some­how returned home. 

“Wald’s work on air­craft vul­ner­ab­il­ity, using data from sur­viv­ors, was cru­cial in World War II, Korea, and Vietnam, and has been reis­sued by the Center for Naval Analyses.”
Source: Journal of the American Statistical Association ¹Mangel, M., & Samaniego, F. (1984). Abraham Wald’s Work on Aircraft Survivability. Journal of the American Statistical Association, 79, 259 – 267. https://​doi​.org/​1​0​.​1​0​8​0​/​0​1​6​2​1​4​5​9​.​1​9​8​4​.​1​0​4​7​8​038

If it had been pos­sible to study all the planes shot down and des­troyed by the Nazis, maybe the research team would have found an inverse pattern?

WWI and the Soldiers’ Helmets

In anoth­er example, we focus on a seem­ingly coun­ter­in­tu­it­ive phe­nomen­on that emerged dur­ing World War I. 

As mil­it­ary forces began to issue more hel­mets to sol­diers to pro­tect them from the per­ils of the bat­tle­field, an unex­pec­ted trend was observed: the num­ber of wounded sol­diers surged dramatically.

At first glance, it may seem that the hel­mets were para­dox­ic­ally mak­ing it easi­er for more people to get hurt. The crux of this para­dox lies in the shift­ing dynam­ics of the cas­u­alty statistics: 

As hel­mets were intro­duced and dis­trib­uted more widely, they sig­ni­fic­antly reduced the num­ber of fatal­it­ies on the bat­tle­field. Soldiers who would have pre­vi­ously suc­cumbed to their injur­ies were now sur­viv­ing. Consequently, these sol­diers were clas­si­fied as “wounded” rather than “dead.”

This shift in cat­egor­isa­tion cre­ated the illu­sion of increased injur­ies when, in real­ity, the num­ber of fatal­it­ies had decreased. 

Survivorship Bias

Survivorship bias occurs when an indi­vidu­al focuses on suc­cess­ful examples or out­comes while ignor­ing those who failed or did not sur­vive. This select­ive focus leads to a skewed under­stand­ing of real­ity, as it fails to account for the lar­ger, often hid­den, set of fail­ures that could provide valu­able insights.

Survivorship bias (example): “Most suc­cess­ful tech star­tups are based in Silicon Valley, so the best way to guar­an­tee our suc­cess is to move our busi­ness there.”

In a busi­ness con­text, sur­viv­or­ship bias can dis­tort decision-mak­ing by pro­mot­ing overly optim­ist­ic or one-sided views of suc­cess. By only high­light­ing the sur­viv­ors or suc­cess stor­ies, organ­iz­a­tions may over­look the chal­lenges and risks that oth­ers faced and miss oppor­tun­it­ies for learn­ing from past failures.

To avoid the pit­falls of sur­viv­or­ship bias, busi­ness lead­ers must take a more com­pre­hens­ive approach to eval­u­at­ing suc­cess. This involves con­sid­er­ing suc­cesses and fail­ures, ana­lyz­ing what led to both out­comes and apply­ing those les­sons to future strategies. 

By embra­cing a bal­anced per­spect­ive that acknow­ledges tri­umphs and set­backs, organ­isa­tions can make more informed, real­ist­ic decisions and avoid mis­guided assump­tions based on incom­plete data.

The loudest voices often belong to those who made it through, but the silence of those who didn’t is the true meas­ure of what was left behind.

Read also: Survivorship Bias

Correlation is Not Causation

Survivorship bias under­scores the import­ance of con­sid­er­ing the lar­ger con­text and being cau­tious about con­clu­sions based solely on super­fi­cial data.

Both stor­ies are excel­lent examples of a par­tic­u­lar fal­lacy — sur­viv­or­ship bias.

“Survivorship bias, sur­viv­al bias is the logic­al error of con­cen­trat­ing on entit­ies that passed a selec­tion pro­cess while over­look­ing those that did not. This can lead to incor­rect con­clu­sions because of incom­plete data.”
Source: Wikipedia ²Survivorship bias. (2023, October 9). In Wikipedia. https://​en​.wiki​pe​dia​.org/​w​i​k​i​/​S​u​r​v​i​v​o​r​s​h​i​p​_​b​ias

We often can’t help being enticed by suc­cess stor­ies in pop­u­lar cul­ture and busi­ness. But their stor­ies can lead us to fal­la­cious conclusions. 

Few think about study­ing those who turn out to be unsuc­cess­ful. This is too bad for data sets since the unsuc­cess­ful typ­ic­ally out­num­ber the suc­cess­ful by a vast margin.

Survivorship bias (men­tal mod­el). This cog­nit­ive bias involves focus­ing on people or things that have “sur­vived” some pro­cess and inad­vert­ently over­look­ing those that did not due to their lack of vis­ib­il­ity. This can lead to false con­clu­sions because it ignores the exper­i­ences of those who did not make it through the pro­cess. ³Silfwer, J. (2019, October 17). Survivorship Bias. Doctor Spin | The PR Blog. https://​doc​tor​spin​.net/​s​u​r​v​i​v​o​r​s​h​i​p​-​b​i​as/

False Causality

False caus­al­ity: “Our sales increased dra­mat­ic­ally after we launched the new logo. Therefore, the new logo must be the reas­on for the sales boost.”

The false caus­al­ity fal­lacy, also known as cor­rel­a­tion, does not imply caus­a­tion and occurs when an indi­vidu­al assumes that just because two events occurred togeth­er, one must have caused the oth­er. This fal­lacy ignores the pos­sib­il­ity that oth­er factors might be at play or that the cor­rel­a­tion could be coincidental.

In busi­ness, false caus­al­ity can lead to mis­guided strategies and decisions, as organ­iz­a­tions may attrib­ute suc­cess or fail­ure to the wrong factors. By over­sim­pli­fy­ing cause-and-effect rela­tion­ships, com­pan­ies risk imple­ment­ing inef­fect­ive strategies or over­look­ing the prop­er drivers behind their outcomes.

To avoid the pit­falls of false caus­al­ity, busi­ness lead­ers must crit­ic­ally exam­ine the rela­tion­ship between events and seek to under­stand the full range of factors that could be influ­en­cing out­comes. Thorough ana­lys­is, using data and evid­ence, is essen­tial to identi­fy real causes and sep­ar­ate them from coin­cid­ent­al cor­rel­a­tions. This pro­motes smarter decision-mak­ing and ensures strategies are based on sound reas­on­ing and a deep under­stand­ing of the busi­ness environment.

Learn more: Logical Fallacies and Cognitive Biases

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Psychology in Public Relations

• Logical Fallacies and Cognitive Biases
• Cognitive Dissonance
• Survivorship Bias 
• Theory of Mind: A PR Superpower
• The Amplification Hypothesis
• The Online Brain Hypothesis
• The Baader-Meinhof Phenomenon: Heard About It Before?
• The Halo Effect
• The Paradox of Prominence
• Parkinson’s Law & The Law of Triviality (Bike Shed Effect)

Group Psychology

• Social Group Sizes (The Social Brain Hypothesis)
• The Pitfalls of Groupthink
• The Engagement Pyramid
• 150 — Dunbar’s Number
• Seriality (Context Matters)
• Diffusion of Innovations
• The Bystander Effect

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