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Statistics
Concept Version 6
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Chance Error and Bias

Chance error and bias are two different forms of error associated with sampling.

Learning Objective

  • Differentiate between random, or chance, error and bias


Key Points

    • The error that is associated with the unpredictable variation in the sample is called a random, or chance, error. It is only an "error" in the sense that it would automatically be corrected if we could survey the entire population.
    • Random error cannot be eliminated completely, but it can be reduced by increasing the sample size.
    • A sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others.
    • There are various types of bias, including selection from a specific area, self-selection, pre-screening, and exclusion.

Terms

  • bias

    (Uncountable) Inclination towards something; predisposition, partiality, prejudice, preference, predilection.

  • random sampling

    a method of selecting a sample from a statistical population so that every subject has an equal chance of being selected

  • standard error

    A measure of how spread out data values are around the mean, defined as the square root of the variance.


Full Text

Sampling Error

In statistics, a sampling error is the error caused by observing a sample instead of the whole population. The sampling error can be found by subtracting the value of a parameter from the value of a statistic. The variations in the possible sample values of a statistic can theoretically be expressed as sampling errors, although in practice the exact sampling error is typically unknown.

In sampling, there are two main types of error: systematic errors (or biases) and random errors (or chance errors).

Random/Chance Error

Random sampling is used to ensure that a sample is truly representative of the entire population. If we were to select a perfect sample (which does not exist), we would reach the same exact conclusions that we would have reached if we had surveyed the entire population. Of course, this is not possible, and the error that is associated with the unpredictable variation in the sample is called random, or chance, error. This is only an "error" in the sense that it would automatically be corrected if we could survey the entire population rather than just a sample taken from it. It is not a mistake made by the researcher.

Random error always exists. The size of the random error, however, can generally be controlled by taking a large enough random sample from the population. Unfortunately, the high cost of doing so can be prohibitive. If the observations are collected from a random sample, statistical theory provides probabilistic estimates of the likely size of the error for a particular statistic or estimator. These are often expressed in terms of its standard error:

$\displaystyle SE_{\bar{x}} = \frac{s}{\sqrt{n}}$

Bias

In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others. It results in a biased sample, a non-random sample of a population in which all individuals, or instances, were not equally likely to have been selected. If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling.

There are various types of sampling bias:

  • Selection from a specific real area. For example, a survey of high school students to measure teenage use of illegal drugs will be a biased sample because it does not include home-schooled students or dropouts.
  • Self-selection bias, which is possible whenever the group of people being studied has any form of control over whether to participate. Participants' decision to participate may be correlated with traits that affect the study, making the participants a non-representative sample. For example, people who have strong opinions or substantial knowledge may be more willing to spend time answering a survey than those who do not.
  • Pre-screening of trial participants, or advertising for volunteers within particular groups. For example, a study to "prove" that smoking does not affect fitness might recruit at the local fitness center, but advertise for smokers during the advanced aerobics class and for non-smokers during the weight loss sessions.
  • Exclusion bias, or exclusion of particular groups from the sample. For example, subjects may be left out if they either migrated into the study area or have moved out of the area.
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