Summary
Survivorship bias or 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. Survivorship bias is a form of selection bias that can lead to overly optimistic beliefs because multiple failures are overlooked, such as when companies that no longer exist are excluded from analyses of financial performance. It can also lead to the false belief that the successes in a group have some special property, rather than just coincidence as in correlation "proves" causality. Another kind of survivorship bias would involve thinking that an incident was not all that dangerous because the only people who were involved in the incident who can speak about it are those who survived it. Even if one knew that some people are dead, they would not have their voice to add to the conversation, leading to bias in the conversation. The parapsychology researcher Joseph Banks Rhine believed he had identified the few individuals from hundreds of potential subjects who had powers of extra-sensory perception (ESP). His calculations were based on the improbability of these few subjects guessing the Zener cards shown to a partner by chance. A major criticism that surfaced against his calculations was the possibility of unconscious survivorship bias in subject selections. He was accused of failing to take into account the large effective size of his sample (all the people he rejected as not being "strong telepaths" because they failed at an earlier testing stage). Had he done this he might have seen that, from the large sample, one or two individuals would probably achieve purely by chance the track record of success he had found. Writing about the Rhine case in Fads and Fallacies in the Name of Science, Martin Gardner explained that he did not think the experimenters had made such obvious mistakes out of statistical naivety, but as a result of subtly disregarding some poor subjects.
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related courses (5)
CS-401: Applied data analysis
This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the dat
BIO-449: Understanding statistics and experimental design
This course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental d
CS-101: Advanced information, computation, communication I
Discrete mathematics is a discipline with applications to almost all areas of study. It provides a set of indispensable tools to computer science in particular. This course reviews (familiar) topics a
Show more