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Usability & InteractionMember ingredient
Survivorship Bias (selection problem)
Analysing only units still visible after entry, attrition, failure or removal can distort rates and relationships because the missing units may differ systematically.
Public evidence overview
One checked source, with its limits.
This preview is not the complete evidence base. Use it as a question to investigate in your own product, not as a prediction of what an individual will do.
Checked source previewAcademic paper
Collider Bias Undermines Our Understanding of COVID-19 Disease Risk and Severity
Research-assisted source review
- What it supports
- People tested for COVID-19 were highly selected across many traits, and plausible selection mechanisms could induce or reverse associations in tested or hospitalised samples.
- Where it may not transfer
- Early-pandemic policies and UK Biobank participation limit direct generalisation. Sensitivity models demonstrate plausibility rather than identify the exact bias in every published estimate.
See how sources and limitations are checked in our editorial method.
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