Editing Sampling (statistics) (section)

==Sampling frame==
{{main|Sampling frame }}
In the most straightforward case, such as the sampling of a batch of material from production (acceptance sampling by lots), it would be most desirable to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not usually possible or practical. There is no way to identify all rats in the set of all rats. Where voting is not compulsory, there is no way to identify which people will vote at a forthcoming election (in advance of the election). These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory.

As a remedy, we seek a [[sampling frame]] which has the property that we can identify every single element and include any in our sample.<ref name="Robert M. Groves, et al">{{cite book
|author= Robert M. Groves|title=''Survey methodology''
|isbn=978-0470465462|display-authors=etal|date=2009
|publisher=John Wiley & Sons
}}</ref><ref>{{cite book
|author=Lohr, Sharon L.|author-link= Sharon Lohr
|title=Sampling: Design and analysis
}}</ref><ref>{{cite book
 |author=Särndal, Carl-Erik |author2=Swensson, Bengt |author3=Wretman, Jan
|title=Model Assisted Survey Sampling
}}</ref><ref>{{cite book
|author= Scheaffer, Richard L. |author2=William Mendenhal |author3=R. Lyman Ott.
|title= Elementary survey sampling |year= 2006 |url= https://archive.org/details/elementarysurvey00sche_0
|url-access= registration
}}</ref> The most straightforward type of frame is a list of elements of the population (preferably the entire population) with appropriate contact information. For example, in an [[opinion poll]], possible sampling frames include an [[electoral register]] and a [[telephone directory]].

A '''probability sample''' is a sample in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. The combination of these traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units according to their probability of selection.

<blockquote>
''Example: We want to estimate the total income of adults living in a given street. We visit each household in that street, identify all adults living there, and randomly select one adult from each household. (For example, we can allocate each person a random number, generated from a [[uniform distribution (continuous)|uniform distribution]] between 0 and 1, and select the person with the highest number in each household). We then interview the selected person and find their income.''

''People living on their own are certain to be selected, so we simply add their income to our estimate of the total. But a person living in a household of two adults has only a one-in-two chance of selection. To reflect this, when we come to such a household, we would count the selected person's income twice towards the total. (The person who ''is'' selected from that household can be loosely viewed as also representing the person who ''isn't'' selected.)''
</blockquote>

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|simple random sampling]], [[systematic sampling]], [[stratified sampling]], probability-proportional-to-size sampling, and [[Cluster sampling|cluster]] or [[multistage sampling]]. These various ways of probability sampling have two things in common:
# Every element has a known nonzero probability of being sampled and
# involves random selection at some point.

===Nonprobability sampling===
{{main|Nonprobability sampling}}
'''Nonprobability sampling''' is any sampling method where some elements of the population have ''no'' chance of selection (these are sometimes referred to as 'out of coverage'/'undercovered'), or where the probability of selection cannot be accurately determined. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements is nonrandom, nonprobability sampling does not allow the estimation of sampling errors. These conditions give rise to [[selection bias|exclusion bias]], placing limits on how much information a sample can provide about the population. Information about the relationship between sample and population is limited, making it difficult to extrapolate from the sample to the population.

<blockquote>
''Example: We visit every household in a given street, and interview the first person to answer the door. In any household with more than one occupant, this is a nonprobability sample, because some people are more likely to answer the door (e.g. an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls) and it's not practical to calculate these probabilities.''
</blockquote>

Nonprobability sampling methods include [[convenience sampling]], [[quota sampling]], and [[purposive sampling]]. In addition, nonresponse effects may turn ''any'' probability design into a nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled.