Survivorship bias

In finance, survivorship bias is the tendency for failed companies to be excluded from performance studies because they no longer exist. It often causes the results of studies to skew higher because only companies that were successful enough to survive until the end of the period are included. For example, a mutual fund company's selection of funds today will include only those that are successful now. Many losing funds are closed and merged into other funds to hide poor performance. In theory, 70% of extant funds could truthfully claim to have performance in the first quartile of their peers, if the peer group includes funds that have closed.[7]

In 1996, Elton, Gruber, and Blake showed that survivorship bias is larger in the small-fund sector than in large mutual funds (presumably because small funds have a high probability of folding).[8] They estimate the size of the bias across the U.S. mutual fund industry as 0.9% per annum, where the bias is defined and measured as:

"Bias is defined as average α for surviving funds minus average α for all funds"

(Where α is the risk-adjusted return over the S&P 500. This is the standard measure of mutual fund out-performance).

Additionally, in quantitative backtesting of market performance or other characteristics, survivorship bias is the use of a current index membership set rather than using the actual constituent changes over time. Consider a backtest to 1990 to find the average performance (total return) of S&P 500 members who have paid dividends within the previous year. To use the current 500 members only and create a historical equity line of the total return of the companies that met the criteria would be adding survivorship bias to the results. S&P maintains an index of healthy companies, removing companies that no longer meet their criteria as a representative of the large-cap U.S. stock market. Companies that had healthy growth on their way to inclusion in the S&P 500 would be counted as if they were in the index during that growth period, which they were not. Instead there may have been another company in the index that was losing market capitalization and was destined for the S&P 600 Small-cap Index that was later removed and would not be counted in the results. Using the actual membership of the index and applying entry and exit dates to gain the appropriate return during inclusion in the index would allow for a bias-free output.

Framed quotes of successful CEOs in a public library

Michael Shermer in Scientific American[9] and Larry Smith of the University of Waterloo[10] have described how advice about commercial success distorts perceptions of it by ignoring all of the businesses and college dropouts that failed.[11] Journalist and author David McRaney observes that the "advice business is a monopoly run by survivors. When something becomes a non-survivor, it is either completely eliminated, or whatever voice it has is muted to zero".[12]

In his book The Black Swan, financial writer Nassim Taleb called the data obscured by survivorship bias "silent evidence".[13]

Diagoras of Melos was asked concerning paintings of those who had escaped shipwreck: "Look, you who think the gods have no care of human things, what do you say to so many persons preserved from death by their especial favour?", to which Diagoras replied: "Why, I say that their pictures are not here who were cast away, who are by much the greater number."[14]

Susan Mumm has described how survival bias leads historians to study organisations that are still in existence more than those that have closed. This means large, successful organisations such as the Women's Institute, which were well organised and still have accessible archives for historians to work from, are studied more than smaller charitable organisations, even though these may have done a great deal of work.[15]

Just as new buildings are being built every day and older structures are constantly torn down, the story of most civil and urban architecture involves a process of constant renewal, renovation, and revolution. Only the most (subjectively, but popularly determined) beautiful, most useful, and most structurally sound buildings survive from one generation to the next. This creates another selection effect where the ugliest and weakest buildings of history have long been eradicated from existence and thus the public view, and so it leaves the visible impression, seemingly correct but factually flawed, that all buildings in the past were both more beautiful and better built.

Whether it be movie stars, athletes, musicians, or CEOs of multibillion-dollar corporations who dropped out of school, popular media often tells the story of the determined individual who pursues their dreams and beats the odds. There is much less focus on the many people that may be similarly skilled and determined but fail to ever find success because of factors beyond their control or other (seemingly) random events.[16] This creates a false public perception that anyone can achieve great things if they have the ability and make the effort. The overwhelming majority of failures are not visible to the public eye, and only those who survive the selective pressures of their competitive environment are seen regularly.

During World War II, the statistician Abraham Wald took survivorship bias into his calculations when considering how to minimize bomber losses to enemy fire.[17] The Statistical Research Group (SRG) at Columbia University, which Wald was a part of, examined the damage done to aircraft that had returned from missions and recommended adding armor to the areas that showed the least damage.[18][19][20] The bullet holes in the returning aircraft represented areas where a bomber could take damage and still fly well enough to return safely to base. Therefore, Wald proposed that the Navy reinforce areas where the returning aircraft were unscathed,[17]^: 88 inferring that planes hit in those areas were the ones most likely to be lost. His work is considered seminal in the then nascent discipline of operational research.[21]

In a study performed in 1987, it was reported that cats who fall from less than six stories, and are still alive, have greater injuries than cats who fall from higher than six stories.[22][23] It has been proposed that this might happen because cats reach terminal velocity after righting themselves at about five stories, and after this point they relax, leading to less severe injuries in cats who have fallen from six or more stories.[24] In 1996, The Straight Dope newspaper column proposed that another possible explanation for this phenomenon would be survivorship bias. Cats that die in falls are less likely to be brought to a veterinarian than injured cats, and thus many of the cats killed in falls from higher buildings are not reported in studies of the subject.[25]

Tropical vines and lianas are often viewed as macro-parasites of trees that reduce host tree survival. The proportion of trees infested with lianas was observed to be much greater in shade-tolerant, heavy wooded, slow-growing tree species while light-demanding, lighter wooded and fast-growing species are often liana free. Such observations led to the expectation that lianas have stronger negative effects on shade-tolerant species.[26] Further investigations, however, revealed that liana infestation is far more harmful to light-demanding fast-growing tree species where liana infestation greatly decreases survival such that the observable sample is biased towards those that survived and are liana-free.[27] Hence, the observable sample of trees with lianas in their crown is skewed due to survivorship bias.

Large groups of organisms called clades that survive a long time are subject to various survivorship biases such as the "push of the past", generating the illusion that clades in general tend to originate with a high rate of diversification that then slows through time.[28]

Survivorship bias can raise truth-in-advertising issues when the success rate advertised for a product or service is measured by reference to a population whose makeup differs from that of the target audience for the advertisement. This is especially important when

1. the advertisement either fails to disclose the relevant differences between the two populations, or describes them in insufficient detail; and
2. these differences result from the company's deliberate "pre-screening" of prospective customers to ensure that only customers with traits increasing their likelihood of success are allowed to purchase the product or service, especially when the company's selection procedures or evaluation standards are kept secret; and
3. the company offering the product or service charges a fee, especially one that is non-refundable or not disclosed in the advertisement, for the privilege of attempting to become a customer.

For example, the advertisements of online dating service eHarmony.com pass this test because they fail the first two prongs but not the third:

1. they claim a success rate significantly higher than that of competing services while generally not disclosing that the rate is calculated with respect to a viewership subset of individuals who possess traits that increase their likelihood of finding and maintaining relationships and lack traits that pose obstacles to their doing so, and
2. the company deliberately selects for these traits by administering a lengthy pre-screening process designed to reject prospective customers who lack the former traits or possess the latter ones, but
3. the company does not charge a fee for administration of its pre-screening test; thus its prospective customers face no "downside risk" other than wasting their time, expending the effort involved in completing the pre-screening process,[29] and suffering disappointment.