{"id":73151,"date":"2022-07-29T11:35:00","date_gmt":"2022-07-29T15:35:00","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=73151"},"modified":"2022-08-01T12:07:54","modified_gmt":"2022-08-01T16:07:54","slug":"survivorship-bias-statistics","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/survivorship-bias-statistics\/","title":{"rendered":"Survivorship Bias in Statistics Skews Results"},"content":{"rendered":"\n

What is survivorship bias<\/a> in statistics? How does survivorship bias skew our interpretation of the research findings? <\/p>\n\n\n\n

Any time a portion of a study sample is able to \u201cleave\u201d the study, we should be wary of survivorship bias. Survivorship bias occurs when we draw conclusions based on the \u201csurvivors\u201d of a certain treatment or intervention. <\/p>\n\n\n\n

Keep reading to learn about survivorship bias and how it affects research findings.<\/p>\n\n\n\n\n\n\n\n

Survivorship Bias<\/strong><\/h2>\n\n\n\n

In statistics, survivorship bias happens when our sample consists of only those who remain at the end of a \u201ctreatment\u201d or over a significant period of time. For example, if you were an aerobics instructor, you might be interested in improving your instruction by collecting data on what people think of your class. If you collected data at the end of a class of regular clients, all of whom have been coming to class for a long time, your sample would be biased toward a positive response. Those who did not enjoy the class would have dropped out, and therefore would not be part of the sample. By sampling a class of regulars you\u2019d be left with \u201csurvivors\u201d who enjoy your instruction and likely have positive things to say.<\/p>\n\n\n\n

Survivorship Bias and the Likelihood of Success<\/strong>

In Fooled by Randomness<\/em><\/a>, <\/em>Nassim Nicholas Taleb<\/a> introduces a different usage of the term \u201csurvivorship bias\u201d<\/a> with a similar consequence to the more traditional definition offered by Wheelan. 

As Wheelan notes, survivorship bias can make groups of people or interventions appear more \u201csuccessful\u201d than they were. For example, if you collected data on the percent of D1 collegiate soccer players who go pro, you would have a skewed perception<\/a> of how many soccer players actually make it to this level, since you haven\u2019t included all of the people who dropped out of the sport between Pee Wee soccer camp and college. 

Taleb offers a similar definition of the term outside the formal research context. He explains that it’s often the stories of success<\/a> that \u201csurvive\u201d their way to public attention. For example, an aspiring actress who moves to Hollywood and survives on a shoestring budget only to eventually win an Oscar would be a memorable story that people would retell. Examples of wild success like this one make their way into the mainstream and skew people\u2019s perceived likelihood of success, making outcomes like this seem more plausible than they really are. In both cases, perceptions of the statistical likelihood of an outcome are skewed. <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Healthy-User Bias<\/a>: <\/strong>Samples can also be biased because of the types of people<\/a> who engage in the treatment we’re interested in studying. As Wheelan explains, the people who choose to engage in whichever activity or habit that we’re collecting data on are likely to be different in significant ways from people who don’t engage in the \u201ctreatment.\u201d Therefore, isolating whether a treatment actually accounts for differences between individuals becomes challenging. <\/p>\n\n\n\n

For example, say we were interested in studying the relationship between swimming and health in senior citizens. We would need to be cognizant of the fact that seniors who are swimming are likely different from their average peers outside of the pool as well. Seniors who are taking the time to swim likely take care of their health in other aspects of their life, such as their diet. Additionally, those seniors who are able to swim into old age are likely in better physical condition than their peers to begin with, hence their continued participation in sports.<\/p>\n","protected":false},"excerpt":{"rendered":"

What is survivorship bias in statistics? How does survivorship bias skew our interpretation of the research findings?  Any time a portion of a study sample is able to \u201cleave\u201d the study, we should be wary of survivorship bias. Survivorship bias occurs when we draw conclusions based on the \u201csurvivors\u201d of a certain treatment or intervention.  Keep reading to learn about survivorship bias and how it affects research findings.<\/p>\n","protected":false},"author":7,"featured_media":36055,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[25],"tags":[681],"class_list":["post-73151","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-statistics","tag-naked-statistics","","tg-column-two"],"yoast_head":"\nSurvivorship Bias in Statistics Skews Results - Shortform Books<\/title>\n<meta name=\"description\" content=\"Any time a portion of a sample is able to \u201cleave\u201d the study, we should be wary of survivorship bias. 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