Before Differentiating the Sampling and Non Sampling Errors, let us define the Error term first.
The difference between an estimated value and the population’s true value is called an error. Since a sample estimate is used to describe a characteristic of a population. A sample being only a part of the population cannot provide a perfect representation of the population, no matter how carefully the sample is selected. Generally, it is seen that an estimate is rarely equal to the true value and we may think about how close will the sample estimate be to the population’s true value.
Two Kinds of Errors: Sampling and Non Sampling Errors
There are two kinds of errors, namely (I) Sampling Errors and (II) Non Sampling Errors
- Sampling Errors (random error)
- Non-Sampling Errors (non-random errors)
- Sampling Errors
A Sampling Error is the difference between the value of a statistic obtained from an observed random sample and the value of the corresponding population parameter being estimated. Let $T$ be the sample statistic used to estimate the population parameter, the sampling error denoted by $E$ is $E = T −\theta$. The value of Sampling Errors reveals the precision of the estimate. The smaller the sampling error, the greater will be the precision of the estimate. The sampling error can be reduced:i) By increasing the sample size
ii) By improving the sampling design
iii) By using the supplementary information - Non Sampling Error
The errors that are caused by sampling the wrong population of interest and by response bias, as well as those made by an investigator in collecting analysis and reporting the data, are all classified as non-sampling errors or non-random errors. These errors are present in a complete census as well as in the sampling survey.