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Selection bias in observational and experimental studies
Author(s) -
Ellenberg Jonas H.
Publication year - 1994
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780130518
Subject(s) - observational study , selection bias , selection (genetic algorithm) , spurious relationship , variety (cybernetics) , data science , observational methods in psychology , underpinning , computer science , psychology , sample (material) , sample size determination , statistics , artificial intelligence , mathematics , civil engineering , chemistry , chromatography , machine learning , engineering
There has been a heightened awareness of the dangers of selection bias over the past two decades. Certainly coverage in statistical and ‘statistics for medicine’, and epidemiology textbooks have allocated pages to warn investigators and readers of investigations to be aware of its presence. The scientific community has not, however, yet accepted the necessity for critical assessment of the method of sample selection in the planning and execution of studies as a fundamental underpinning of observational and experimental studies. To wit, we are faced with a plethora of research studies receiving funding, being published in peer‐reviewed journals and influencing future studies, that may be reporting entirely spurious associations. It is the intent of this paper to present examples of selection bias in a variety of areas which have resulted in misleading or entirely incorrect results. We hope to help make such research scientifically ‘politically incorrect’ to the degree that the scientific community ‘just says no’ to such studies, either proposed or reported.