For example, in the case of political polling, some people are not contactable, and others refuse to participate.
In practice, nearly all samples are non-probability samples. For example, with s quota sampling, if randomly contacting people and continuing to do so until all quotas are met, this approach can lead to a sample that is approximately random (Seymour Sudman, Applied Sampling, Academic Press, 1976). Nevertheless, when significant randomization does occur within non-random samples, they can have properties that are closer to random samples. For this reason, the statistical literature often describes non-probability samples as mistakes. There is typically no mechanism for quantifying the extent to which conclusions from a random sample may diverge from the population. However, the benefit of random sampling is that the level of uncertainty (sampling error) is quantifiable using statistical theory. In theory, a non-random sample can be just as representative as a random sample. It is useful in situations where participants for a study are hard to find (e.g., studies of illegal drug users, people with HIV), Statistical analysis of non-random samples Snowball sampling is a technique where a respondent nominates other people to participate in the study. Samples designed to maximize variation within the sample, expert samples, and samples of “typical” people are all types of purposive samples. For example, whereas a simple random sample may obtain 50% men and 50% women, a purposive sample may seek to represent all genders (including transgender people). Purposive sampling involves obtaining a sample such that it maximizes the quality of the information obtained from a sample, rather than representing the population at large.
#Simple random sampling method example tv
This could be via phone and text message-based campaigns run by TV and radio stations, or questionnaires on newspaper websites.
Volunteer sampling involves asking for people to volunteer to participate. Common examples of this would be conducting interviews in high-traffic locations, or among students. Convenience samplingĬonvenience sampling refers to approaches where considerations of simplicity rather than randomness determine which observations are selected in a sample. The main alternative to random sampling is quota sampling. This involves specifying required sub-samples, and obtaining these in a cost-effective way (e.g., obtaining 50 males under 30, 50 females under 30, 50 males 30 or older, and 50 females 30 or older). It is common practice to use as much randomization as possible when employing these techniques, in the hope that the resulting sample approximates the qualities of a random sampling. Specific types of non-random sampling include quota sampling, convenience sampling, volunteer sampling, purposive sampling, and snowball sampling. A sample that is not a random sample is known as a non-random or non-probability sample. For example, in surveys involving humans, it is usually not practical to contact most people, let alone to compel them to participate if randomly selected.Ĭonsequently, many alternatives exist to random sampling. Although the concept of random sampling is central to much of statistical theory, in practice it is rare. Sampling refers to the process of selecting a sample. A random sample is a subset of individuals selected at random from a larger population, where each individual in the population has a known and non-zero chance of being chosen.
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