Sampling bias
Sampling bias (pronunciation: /ˈsæmplɪŋ baɪəs/) is a type of bias that occurs when certain members of the intended population are less likely to be included than others in a statistical sample.
Etymology
The term "sampling bias" is derived from the English words "sampling" and "bias". "Sampling" (from the Old English samplian, meaning "to take a sample") refers to the process of selecting a subset of individuals from a statistical population to estimate characteristics of the whole population. "Bias" (from the Old French biais, meaning "slant, slope, oblique") in this context refers to a systematic deviation from the true value.
Types of Sampling Bias
There are several types of sampling bias, including:
- Selection bias: This occurs when the method of selecting samples causes the sample to differ significantly from the population. For example, if a survey about health habits is conducted at a gym, it may over-represent individuals who are healthier and more active than the general population.
- Non-response bias: This occurs when individuals selected for the sample do not respond or cannot be contacted. Non-response bias can lead to an over- or under-estimation of certain characteristics in the population.
- Undercoverage bias: This occurs when some groups of the population are inadequately represented in the sample. For example, a telephone survey may under-represent individuals who do not own a telephone.
Prevention and Control
Sampling bias can be prevented or controlled through various methods, such as:
- Random sampling: This is a method where each member of the population has an equal chance of being selected. This can help to ensure that the sample is representative of the population.
- Stratified sampling: This is a method where the population is divided into subgroups or strata, and random samples are taken from each stratum. This can help to ensure that all groups are adequately represented in the sample.
See Also
External links
- Medical encyclopedia article on Sampling bias
- Wikipedia's article - Sampling bias
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