An analyst makes a statistical error known as a sampling error when they choose a sample that does not accurately represent the complete population of data. As a result, the sample’s findings do not accurately reflect the findings from the total population.
Sampling is a type of analysis where a small sample of observations are chosen from a larger population. Both sampling errors and non-sampling mistakes can be produced by the selection process.
The difference between the sampled value and the actual population value is known as a sampling error. Because the sample is not typical of the population or is prejudiced in some way, sampling errors happen. Due to the fact that a sample is merely an approximate representation of the population from which it is collected, even randomized samples will contain some level of sampling error. The overall sampling error in statistical analysis is determined using the sampling error formula. By multiplying the result by the Z-score value, which is based on the confidence interval, the sampling error is computed by dividing the standard deviation of the population by the square root of the sample size.