Sampling Error Definition
Sampling error is defined as the amount of incorrect information in estimating a particular value, resulting from considering a small portion of the population, called the sample, instead of the entire population. A sample survey focuses on surveying a small portion of the population, this means that there is always a large amount of error in the resulting data since large amount of data is not being considered .This uncertainty can be interpreted as variable error or sampling error.
Sampling Error: Definition and Formula
“Random variation” or “random error” is inherent in predictive statistical models. It is defined as the difference between the expected value of the variable (according to the statistical model of the problem) and the actual value of the variable. If the sample size is large, these errors are distributed well above and below the mean and then cancel each other out, resulting in the expected value of zero.
This error stands in sharp contrast to another modelling error, the so-called “sampling error.” This is a systematic error that has crept into the system due to biased assumptions or experimental design. Because this error is directly defined by the variable, its expected value is nonzero, creating a serious flaw in the model.
Table of Content
- Sampling Error Definition
- Sampling Error Formula
- How to Reduce Sampling Error?
- Precautions Using Sampling Errors
- Sampling Error Examples
- FAQs on Sampling Error