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Example of biased sample: as of June 2008 55% of web browsers (Internet Explorer) in use did not pass the Acid2 test. Due to the nature of the test, the sample consisted mostly of web developers. [16] A classic example of a biased sample and the misleading results it produced occurred in 1936.
Insensitivity to sample size is a cognitive bias that occurs when people judge the probability of obtaining a sample statistic without respect to the sample size.For example, in one study, subjects assigned the same probability to the likelihood of obtaining a mean height of above six feet [183 cm] in samples of 10, 100, and 1,000 men.
The reason that an uncorrected sample variance, S 2, is biased stems from the fact that the sample mean is an ordinary least squares (OLS) estimator for μ: ¯ is the number that makes the sum = (¯) as small as possible. That is, when any other number is plugged into this sum, the sum can only increase.
Spectrum bias arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the test. For example, a high prevalence of disease in a study population increases positive predictive values, which will cause a bias between the prediction values and the real ones. [4]
Egocentric bias is the tendency to rely too heavily on one's own perspective and/or have a different perception of oneself relative to others. [35] The following are forms of egocentric bias: Bias blind spot, the tendency to see oneself as less biased than other people, or to be able to identify more cognitive biases in others than in oneself. [36]
It is this second step which makes the technique one of non-probability sampling. In quota sampling the selection of the sample is non-random. For example, interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection.
The selective effect of cellular telephones on data collection (discussed in the Overgeneralization section) is one potential example; If young people with traditional telephones are not representative, the sample can be biased. Sample surveys have many pitfalls and require great care in execution. [18]
In reality, obtaining an unbiased sample can be difficult as many parameters (in this example, country, age, gender, and so on) may strongly bias the estimator and it must be ensured that none of these factors play a part in the selection process.