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SAMPLING ERROR
January 14, 2023
Sampling error is the difference between the result obtained from a sample and that which could have been obtained if the entire population is used.
It is measured by the standard error of the statistics in terms of probability under normal curve. This could also occur when the complete survey of the population is not carried out, but sample taken for estimating the characteristics of the population. The smaller the sample error, the greater the precision of the estimate.
The difference between the mean of a sample and the mean of the population, if it were obtained in a type of sampling error and is measured by the standard error of the mean.
It is always necessary to look out for presence of errors while sampling because it serves as an indicator for the level of confidence that is put in the final result of a study. When sample does not fully represent the whole population there tend to be a statistical error in the study. This will affect the entire research and the level of accuracy will diminish.
The formula for sampling error is as follows:
S.E = (1/√N) 100
Where; N = Sample size
S.E = Sampling error
1 = Constant figure