Self-selection bias

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In statistics, self-selection bias arises in any situation in which individuals select themselves into a group, causing a biased sample with nonprobability sampling. It is commonly used to describe situations where the characteristics of the people which cause them to select themselves in the group create abnormal or undesirable conditions in the group. It is closely related to the non-response bias, describing when the group of people responding has different responses than the group of people not responding.

Self-selection bias is a major problem in research in sociology, psychology, economics and many other social sciences.[1] In such fields, a poll suffering from such bias is termed a self-selected listener opinion poll or "SLOP".[2] The term is also used in criminology to describe the process by which specific predispositions may lead an offender to choose a criminal career and lifestyle.



Self-selection makes determination of causation more difficult. For example, significantly higher test scores might be observed among students who participate in a test preparation course. Due to self-selection, there may be a number of differences between the people who choose to take the course and those who choose not to, such as motivation, socioeconomic status, or prior test-taking experience. Due to self-selection according to such factors, a significant difference in mean test scores could be observed between the two populations independent of any ability of the course to effect higher test scores.

Self-selection bias causes problems for research about programs or products. In particular, self-selection affects evaluation of whether or not a given program has some effect, and complicates interpretation of market research.

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  2. ^ Lenskyj, Helen Jefferson (2008). Olympic Industry Resistance: Challenging Olympic Power and Propaganda. State University of New York Press. p. 56. ISBN 978-0-7914-7479-2. 
  • Jacobs, B., Hartog, J., Vijverberg, W. (2009) "Self-selection bias in estimated wage premiums for earnings risk", Empirical Economics, 37 (2), 271–286. doi:10.1007/s00181-008-0231-0

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