What is Sampling Error
Sampling Error refers to the statistical error occurred when the subset of the population i.e. sample deviates from the true characteristics, attributes and behavior of the total population. Simply put, when the sample selected from the population differs from the actual attributes of the target population, that is when sampling error arises.
The measurement of sampling error is usually called the “precision of the sampling plan”. If we increase the sample size, the precision can be improved. But increasing the size of the sample has its own limitations. For example, a large sized sample increases the cost of collecting data, and also increases the systematic bias.
Thus the effective way to increase precision is usually to select a better sampling design which has a smaller error margin for a given sample size at a given cost.
Types of sampling error
There are two types of Sampling Errors. These are:
When the selection of a sample is based on the personal prejudice or bias of the investigator then the results are prone to biased 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 Unbiased Errors arise due to unforeseen circumstances occurring during the study or research. In case of unbiased sampling error, the investigator does not intentionally tamper with the sample. Here, if there are any difference between the population and sample, it has occurred by chance. Unbiased error can also be caused by using sampling techniques that are incompatible to the research.