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Each [[observation]] measures one or more properties (such as weight, location, colour or mass) of independent objects or individuals. In [[survey sampling]], weights can be applied to the data to adjust for the sample design, particularly in [[stratified sampling]].<ref>{{Cite book|url=https://www.measureevaluation.org/resources/publications/ms-16-112|title=Sampling and Evaluation |author=Lance, P. |author2=Hattori, A.|publisher=MEASURE Evaluation|year=2016|location=Web|pages=6–8, 62–64}}</ref> Results from [[probability theory]] and [[statistical theory]] are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population.<ref>Salant, Priscilla, I. Dillman, and A. Don. ''How to conduct your own survey''. No. 300.723 S3. 1994.</ref> [[Acceptance sampling]] is used to determine if a production lot of material meets the governing [[specification]]s.
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In the above example, not everybody has the same probability of selection; what makes it a probability sample is the fact that each person's probability is known. When every element in the population ''does'' have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight.
Probability sampling includes: [[Simple random sample|
# Every element has a known nonzero probability of being sampled and
# involves random selection at some point.
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In social science research, [[snowball sampling]] is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Some variants of snowball sampling, such as respondent driven sampling, allow calculation of selection probabilities and are probability sampling methods under certain conditions.
===Voluntary
{{further|Self-selection bias}}
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Theoretical sampling<ref name=":0">{{Cite web|url = http://www.fao.org/ag/humannutrition/32428-0613f516cb07eade922c8c19b4d0452c0.pdf|title = Examples of sampling methods}}</ref> occurs when samples are selected on the basis of the results of the data collected so far with a goal of developing a deeper understanding of the area or develop theories. Extreme or very specific cases might be selected in order to maximize the likelihood a phenomenon will actually be observable.
=== Active
In [[active sampling]], the samples which are used for training a machine learning algorithm are actively selected, also compare [[active learning (machine learning)]].
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