Sampling bias

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 (or non-human factors) 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.

Types of sampling bias

Sampling bias can be classified into three types: selection bias, exclusion bias, and attrition bias.

• Selection bias occurs when some members of a population are systematically more likely to be selected in a sample than others. It is sometimes referred to as the selection effect. The phrase "selection bias" most often refers to the distortion of a statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may not be accurate.

• Exclusion bias arises from exclusion of particular groups from the sample, e.g. exclusion of people who have recently migrated into the area. This can occur when individuals are excluded from the survey or study due to a particular attribute. This can lead to a bias in the sample population that may not accurately reflect the actual population.

• Attrition bias is a kind of selection bias caused by attrition (loss of participants), discounting trial subjects/tests that did not run to completion. It is a distortion of statistical analysis, due to the method of collection of samples. If the parameter of interest changes over time, it will likely not be accurately captured in the study.

Effects of sampling bias

Sampling bias can lead to a range of erroneous conclusions about the population under study. An overrepresentation of a particular group can lead to an overestimation of the prevalence of certain properties in the population, while an underrepresentation can lead to an underestimation. This can lead to false conclusions about the relationships between different properties.

Preventing sampling bias

There are several strategies to reduce or eliminate sampling bias. These include random sampling, stratified sampling, and systematic sampling. Each of these methods aims to ensure that every member of the population has an equal chance of being included in the sample.

See also

• Bias (statistics)
• Selection bias
• Observer-expectancy effect
• Participation bias
• Self-selection bias
• Survivorship bias

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