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Sampling and non sampling errors.

Definition of 'Sampling Error'
A statistical error to which an analyst exposes a model simply because he or she is working with sample data rather than population or census data. Using sample data presents the risk that results found in an analysis do not represent the results that would be obtained from using data involving the entire population from which the sample was derived.

Types of sampling errors

  • Random sampling errors
In statistics, the sampling error can be found by deducting the value of a parameter from the value of a statistic. This type of sampling error occurs where an estimate of quantity of interest, for example an average or percentage, will generally be subject to sample-to-sample variation. An example of the sampling error in evolution would be a genetic drift – a change in population’s allele frequencies due to chance. The bottleneck effect and the founder effect can be considered as an example of random sampling error.

  • Bias problems
Sampling bias is likely to be a source of sampling errors. The bias problems lead to sampling errors which have a prevalence to be either positive or negative. These types of errors are also considered as systematic errors.
  • POPULATION SPECIFICATION ERROR—This error occurs when the researcher does not understand who she should survey. For example, imagine a survey about breakfast cereal consumption. Who should she survey? It might be the entire family, the mother, or the children. The mother probably makes the purchase decision, but the children influence her choice.
  • SAMPLE FRAME ERROR—A frame error occurs when the wrong sub-population is used to select a sample. A classic frame error occurred in the 1936 presidential election between Roosevelt and Landon. The sample frame was from car registrations and telephone directories. In 1936, many Americans did not own cars or telephones and those who did were largely Republicans. The results wrongly predicted a Republican victory.
  • SELECTION ERROR—This occurs when respondents self select their participation in the study – only those that are interested respond. Selection error can be controlled by going extra lengths to get participation. A typical survey process includes initiating pre-survey contact requesting cooperation, actual surveying, post survey follow-up if a response is not received, a second survey request, and finally interviews using alternate modes such as telephone or person to person.
  • NON-RESPONSE—Non-response errors occur when respondents are different than those who do not respond. This may occur because either the potential respondent was not contacted or they refused to respond. The extent of this non-response error can be checked through follow-up surveys using alternate modes.
  • SAMPLING ERRORS—These errors occur because of variation in the number or representativeness of the sample that responds. Sampling errors can be controlled by (1) careful sample designs, (2) large samples, and (3) multiple contacts to assure representative response. 

Definition of 'Non-Sampling Error'

A statistical error caused by human error to which a specific statistical analysis is exposed. These errors can include, but are not limited to, data entry errors, biased questions in a questionnaire, biased processing/decision making, inappropriate analysis conclusions and false information provided by respondents.


Non-sampling error

One of the two reasons for the difference between an estimate (from a sample) and the true value of a population parameter; the other reason being the error caused because data are collected from a sample rather than the whole population (sampling error). Non-sampling errors have the potential to cause bias in surveys or samples.
There are many different types of non-sampling errors and the names used for each of them are not consistent.
Some examples of non-sampling errors are:
•    The sampling process is such that a specific group is excluded or under-represented in the sample, deliberately or inadvertently. If the excluded or under-represented group is different, with respect to survey issues, then bias will occur.
•    The sampling process allows individuals to select themselves. Individuals with strong opinions about the survey issues or those with substantial knowledge will tend to be over-represented, creating bias.
•    If people who refuse to answer are different, with respect to survey issues, from those who respond then bias will occur. This can also happen with people who are never contacted and people who have yet to make up their mind.
•    If the response rate (the proportion of the sample that takes part in a survey) is low, bias can occur because respondents may tend consistently to have views that are more extreme than those of the population in general.
•    The wording of questions, the order in which they are asked and the number and type of options offered can influence survey results.
•    Answers given by respondents do not always reflect their true beliefs because they may feel under social pressure not to give an unpopular or socially undesirable answer.
•    Answers given by respondents may be influenced by the desire to impress an interviewer.

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