Selection bias

selection effectselectionbiasselection effectssampling biasattrition biasbiasesdata selectiondropoutexclusion bias
Selection bias is the bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed.wikipedia
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Sampling bias

ascertainment biasbiased samplebias
Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in a biased sample, defined as a statistical sample of a population (or non-human factors) in which all participants are not equally balanced or objectively represented.
Sampling bias is mostly classified as a subtype of selection bias, sometimes specifically termed sample selection bias, but some classify it as a separate type of bias.

External validity

externalExternal validity (scientific studies)generalised
A distinction of sampling bias (albeit not a universally accepted one) is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand.
An important variant of the external validity problem deals with selection bias, also known as sampling bias—that is, bias created when studies are conducted on non-representative samples of the intended population.

Sampling (statistics)

samplingrandom samplesample
Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in a biased sample, defined as a statistical sample of a population (or non-human factors) in which all participants are not equally balanced or objectively represented.
These conditions give rise to exclusion bias, placing limits on how much information a sample can provide about the population.

Self-selection bias

self-selectionself-selectedself selection
Examples of sampling bias include self-selection, pre-screening of trial participants, discounting trial subjects/tests that did not run to completion and migration bias by excluding subjects who have recently moved into or out of the study area.
While the effects of self-selection bias are closely related to those of selection bias, the problem arises for rather different reasons; thus there may be a purposeful intent on the part of respondents leading to self-selection bias whereas other types of selection bias may arise more inadvertently, possibly as the result of mistakes by those designing any given study.

Survivorship bias

survivor biasdominated by durable examplessurvival bias
It is closely related to the survivorship bias, where only the subjects that "survived" a process are included in the analysis or the failure bias, where only the subjects that "failed" a process are included.
It is a form of selection bias.

Failure bias

It is closely related to the survivorship bias, where only the subjects that "survived" a process are included in the analysis or the failure bias, where only the subjects that "failed" a process are included.
It is a form of selection bias.

Anthropic principle

weak anthropic principleanthropicAnthropic bias
In situations where the existence of the observer or the study is correlated with the data, observation selection effects occur, and anthropic reasoning is required.
Some critics of the SAP argue in favor of a weak anthropic principle (WAP) similar to the one defined by Brandon Carter, which states that the universe's ostensible fine tuning is the result of selection bias (specifically survivorship bias): i.e., only in a universe capable of eventually supporting life will there be living beings capable of observing and reflecting on the matter.

Heckman correction

Heckit modelHeckman selection correctionHeckman selection model
In the general case, selection biases cannot be overcome with statistical analysis of existing data alone, though Heckman correction may be used in special cases.
The Heckman correction is a statistical technique to correct bias from non-randomly selected samples or otherwise incidentally truncated dependent variables, a pervasive issue in quantitative social sciences when using observational data.

Nick Bostrom

BostromNick Bostrom and superintelligenceProfessor Nick Bostrom
Philosopher Nick Bostrom has argued that data are filtered not only by study design and measurement, but by the necessary precondition that there has to be someone doing a study.
In the 2008 volume Global Catastrophic Risks, editors Bostrom and Milan M. Ćirković characterize the relation between existential risk and the broader class of global catastrophic risks, and link existential risk to observer selection effects and the Fermi paradox.

Publication bias

File drawer problemfile drawer effectself-selecting nature of the positive reports

Bias

biasesunbiasedbiased
Selection bias is the bias introduced by the selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed.

Statistics

statisticalstatistical analysisstatistician
The phrase "selection bias" most often refers to the distortion of a statistical analysis, resulting from the method of collecting samples.

Sample (statistics)

samplesamplesstatistical sample
Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in a biased sample, defined as a statistical sample of a population (or non-human factors) in which all participants are not equally balanced or objectively represented.

Statistical population

populationsubpopulationsubpopulations
Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in a biased sample, defined as a statistical sample of a population (or non-human factors) in which all participants are not equally balanced or objectively represented.

Internal validity

validityinternalhistory
A distinction of sampling bias (albeit not a universally accepted one) is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand.

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