biased sample

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biased sample

A subset of a population that does not represent—either intentionally or unintentionally—an entire population.

biased sample

In epidemiology or medical research, a sample of a group that does not equally represent the members of the group.
See also: sample
References in periodicals archive ?
One of the more recent examples of sample bias for a survey was in the Democratic primary election.
Second, AI's ability to explore large data sets offers opportunities for novel diagnostics and personalized medicine, though this will depend in part on whether traditionally under-represented populations will be sufficiently represented to avoid sample bias. Finally, AI may become a powerful tool to support clinical decision-making by human caregivers, but clinicians must take care not to become overly reliant on AI for this purpose.
Second, the estimation method controls for contemporaneous dependence across individuals and the finite sample bias of estimates, which are generally ignored in the existing literature.
The quasi real time data are also constructed such that the difference between estimates of output gap from real time data and that from quasi real time data reflects data revision and the difference between estimates of output gap from final data and that from quasi real time data portray other revisions including end sample bias. Moreover, output gap is estimated with the help of five methods namely the linear trend method, quadratic trend method, Hordrick-Prescott (HP) filter, production function method, and structural vector autoregressive method.
Last, (http://onlinelibrary.wiley.com/doi/10.1111/jhn.12286/full) one study found that the research supporting detox teas were full of sample bias, small samples, and flawed methodology .
Stambaugh (1999) emphasizes the severity of small sample bias as the reason behind poor predictability.
But when Bristol researchers took a closer look at the research - which took a year to be sent to them under a Freedom of Information request - they identified what they said was missing data, sample bias and poor research design.
However, with regard to sample bias in the strong group, when Wiseman (2003) investigated strength of belief in luck in the United Kingdom, the weak group accounted for 14.0% to 25.8% of the total sample.
Therefore, it would be impossible to reach those people by cell phone, thus creating a sample bias, or such efforts would bring into the sample only those who use cell phones of the country where they reside, thus sampling only those people who are more oriented to work with the country of their residence.
This image was taken with a sample bias and a tunneling current of 1.61V and 0.10 nA, respectively.
Critics of the programs contend that sample sizes were too small, so that the effects found probably reflect sample bias. Heckman counters this objection by pointing out that the Perry and Abecedarian experiments were both randomized trials and have had decades of followup observations.