Sampling Errors Definition | 5 Most common Methods of Minimizing Sampling Errors

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Sampling Errors Definition | 5 Most common Methods of Minimizing Sampling Errors

Sampling Errors

The error that arises as a result of taking a sample from a population rather than using the whole population is Known as sampling error. An estimate of a population parameter, such as a sample mean or sample proportion, is likely to be different for different samples (of the same size) taken from the population and each estimate is likely to be different from the true population parameter. Sampling error is one of two reasons for the difference between an estimate and the true, but unknown value of the population parameter. For example, if one measures the height of a thousand individuals from a country Of one million, the average height of the thousand is typically not the same as the average height of all one million people in the country. Since sampling is typically done to determine the characteristics Of a whole population, the difference between the sample and population value is considered a sampling error. Generally, sampling errors take place in social science research when sample size can not cater to the entire characters of the population. The sampling errors that commonly taken place are given below:
Sampling Errors Definition | 5 Most common Methods of Minimizing Sampling Errors

1. Population specification error: 

This error occurs when the researcher does not understand who should be surveyed. For example, imagine a survey about breakfast cereal consumption. Who should be surveyed? It might be the entire family, the mother, the children. The mother probably makes the purchase decision, but the children influence her choice.

2. Sample frame error: 

A sample frame error occurs when the wrong sub-population is used to select a sample. If we use the telephone directories as the sample frames for conducting research of those people who do not use the telephone then the sample frame is wrong so the prediction of the research will also be incorrect.

3. Selection error: 

This occurs when respondents self-select their participation in the study — only those that are interested to 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 researcher can make a second survey request, and finally interviews using alternate modes such as telephone or face-to-face.

4. 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 refused to respond. The extent of this non-response error can be checked through follow-up surveys using alternate modes.

5. Error in taking sample: 

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 representativeness of response.

Methods of Minimizing Sampling Errors

Population always remains larger so census study is not possible. Thus, the researcher conducts research considering the sample. Obviously, there will be sampling errors while conducting research considering samples. Such errors can be minimized doing following works:

1. Increase sample size: 

An increase in sample size represents more characters of the population. Errors can be minimized if the sample size is increased. A researcher, as far as possible, should increase the sample size to minimize sampling errors.

2. Crosscheck: 

Sample and responses are collected from various sources. Such responses should be checked so that unrelated and biased responses can be removed and errors can be minimized.

3. Unbiased sampling: 

If the samples are selected without any bias or using statistical method then there is the chance of selection of the right samples that helps to minimize errors.

Sampling Errors Definition | 5 Most common Methods of Minimizing Sampling Errors

4. Appropriate sampling design: 

A researcher should prepare a sample plan before selecting a sample. The sampling plan considers the importance and necessity of sample and nature of research so that appropriate sampling is possible that helps to minimize sampling errors.

5. Clear questionnaire: 

A questionnaire should be clear and should not include ambiguous words and complex sentences. If a questionnaire is clear, there are chances of the right response so tha errors can be minimized.

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