Definition: The Sampling Error refers to the statistical error occurred when the subset of the population (sample) deviates from the true characteristics, attributes and behavior of the total population. Simply, when the sample selected from the population differs from the actual attributes of the target population, then the sampling error arises.
Basically, there are two types of Sampling Errors:
- Biased Errors: When the selection of a sample is based on the personal prejudice or bias of the investigator then the results are prone to bias errors. Such as, if the investigator is required to collect the sample using the simple random sampling and instead he used the non-random sampling, then personal prejudice is introduced in the research process that will lead to the biased errors.
The personal prejudice or bias arises due to the faulty methods used for collection, selection and analysis of the data obtained from the target population.
- Unbiased Errors: The Unbiased Errors arise due to a chance, i.e. the investigator has not intentionally tampered with the sample and that the difference between the population and sample have occurred by chance.
Even though the utmost care has been taken in the selection of a sample, the sampling error may occur because the subjects drawn from the population have individual differences. And therefore, the investigator must keep in his mind that only the subset of the population is selected, and hence there will be a difference between the population and a sample.