biased sample

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biased sample

[bī′əst]
Etymology: OFr, biais, slant; L, exemplum, sample
(in research) a sample of a group in which all factors or participants are not equally balanced or objectively represented.

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 ?
Analytical validation of serum proteomic profiling for diagnsosis of prostate cancer: sources of sample bias.
However, most frequently used method of testing for sample bias is to compare the response and Non-response (about 10%), the testing technology is t-test(about 6%) or Chi-square(about 6%)?
d] [is greater than] 2, then the differences observed in the distribution of isolates for given shared types were statistically significant and not due to potential sample bias.
Both the degree of the finite sample bias of the LR tests as well as the ability of Sims's adjustment in correcting the finite-sample bias are investigated by the following experiments.
The swapping procedure has a potential for introducing sample bias as it departs somewhat from purely random sampling, but simulations and evaluations done to date indicate that no bias has resulted.
These biases are approximately additive and result in an overall outlet substitution bias and unrepresentative outlet sample bias for the Canadian CPI in the range of 0.
In contrast, the finite sample bias from using either one of the two above dynamic models tended to be small.
Parental informed consent and sample bias in grade-school children.
The positive bias evident in the sample bias plot in Figure II is more pronounced above the value 150 on the reference method.
The problem was challenging as the model had to overcome the sample bias in the development sample and still be robust enough to deliver accurate results over time.
Topics include structural models and empirical analysis of technology accumulation and diffusion, econometric modeling of value-at-risk, small sample bias corrections for inequality indices, inflation targeting in developing economies, driving factors of Italy's trade flow over the last two decades, labor supply shocks and unemployment persistence in the Netherlands, modeling asymmetric consumer behavior and demand equations for bridging gaps in retailing, and closed-form approximation of likelihood functions for discretely sampled diffusions.
Analytical validation of serum proteomic profiling for diagnosis of PCa: sources of sample bias.