What is Sampling Error?
Sampling error is the difference between a sample statistic and the corresponding population parameter. It is a measure of the variability that is due to the selection of a sample from a population. Sampling error, also known as sampling bias, occurs when a sample statistic does not accurately represent the population parameter, typically due to the fact that the sample does not accurately reflect the population.
Types of Sampling Error
There are two types of sampling error:
- Bias Error – Bias error occurs when the sample does not accurately reflect the population. This type of error can be caused by a variety of factors, such as a researcher’s selection of sampling methods, non-response bias, or the use of a convenience sample instead of a random sample.
- Random Error – Random error is the difference between a sample statistic and the population parameter that is due to chance. This type of error is unavoidable and is due to the fact that the sample is a subset of the population and therefore cannot perfectly represent the population.
Examples of Sampling Error
- A survey of voters’ opinions on a current political issue is conducted using a convenience sample. The results of the survey are not representative of the population as a whole, as the sample does not accurately reflect the population.
- A researcher is interested in the average age of students at their university. They collect a random sample of 100 students, but the results of the sample do not accurately reflect the population as the sample is only a small portion of the population.
- A survey of people’s opinions on a new product is conducted using a biased sample. The results of the survey are not representative of the population as the sample is not randomly chosen and therefore does not accurately reflect the population.
Sampling error can have a significant impact on the reliability of research findings. It is important to ensure that the sample is representative of the population in order to minimize the potential for sampling error.
Conclusion
Sampling error is the difference between a sample statistic and the corresponding population parameter. There are two types of sampling error: bias error and random error. Sampling error can have a significant impact on the reliability of research findings and it is important to ensure that the sample is representative of the population in order to minimize the potential for sampling error.
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