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The 31 pages that link to Sampling bias, each with the reason it gives.

• FossilRelated: Fossil discoveries overrepresent organisms and places that preserve and expose remains.

• Camera trapRelated: Trap locations and detection settings can overrepresent some animals or behaviors.

• Corpus linguisticsRelated: A biased corpus can make unrepresentative language patterns appear typical.

• External validityRelated: Biased recruitment can leave important populations outside a study's evidence.

• Linguistic typologyRelated: Overrepresenting related or well-documented languages can make a pattern look more universal.

• Exploratory data analysisRelated: Visible patterns in a dataset may reflect how observations were selected rather than the wider population.

• Nonresponse biasRelated: It can precede nonresponse, and the two distortions require different remedies.

• Cross-cultural psychologyRelated: Convenience samples can make local findings appear to describe whole cultures.

• Biodiversity monitoringRelated: Biased effort can mimic biodiversity change or conceal it.

• Sampling errorCompared with: Bias is systematic error, unlike the random variation meant by sampling error.

• SexologyRelated: It limits claims drawn from early studies recruited through narrow or self-selected groups.

• Ecological validityRelated: Even realistic procedures may yield limited generalization when the sampled people are unrepresentative.

• La Brea Tar PitsRelated: A trap deposit overrepresents animals attracted to or caught near asphalt seeps.

• Empirical evidenceRelated: Biased selection can make observed evidence misrepresent the world being studied.

• Mesozoic Marine RevolutionRelated: Uneven fossil preservation can make ecological trends appear stronger or weaker than they were.

• Modifiable areal unit problemCompared with: Areal-unit sensitivity can occur even with complete data, unlike bias caused by an unrepresentative sample.

• Species distribution modelingRelated: Uneven survey effort can make recorded occurrences reflect observer access rather than habitat suitability.

• Sample sizeCompared with: More observations do not remove bias introduced by how units entered the sample.

• Alfred KinseyRelated: Critics showed that recruitment methods weakened some broad claims from the reports.

• Modal shareRelated: Nonrepresentative travel samples can misstate a mode's share.

• Molecular epidemiologyRelated: Uneven sampling can make genetic clusters misrepresent actual patterns of spread.

• PaleohistologyRelated: Destructive sampling and uneven fossil preservation can skew histological comparisons.

• Population densityRelated: Biased surveys can distort estimated density even when counts are precise.

• Statistical populationRelated: A sample can misrepresent the population when its selection systematically excludes or favors units.

• Sex researchRelated: Recruitment can skew estimates of sensitive or stigmatized experiences.

• Spatial interpolationRelated: Uneven or selective sampling can make interpolated patterns reflect data collection rather than the phenomenon.

• Evelyn HookerRelated: Hooker’s small, nonrandom sample limits how broadly her results can be generalized.

• Bradley effectRelated: A poll's racial and political composition can distort its estimate independently of dishonest answers.

• Correlation function measurementRelated: Biased observation or pair selection can distort empirical dependence estimates.

• Fish length–weight relationshipRelated: Gear and sampling choices can overrepresent particular sizes and distort fitted parameters.

• SampleCompared with: A biased sample can misrepresent the material it is intended to stand for.