# Sampling and NonSampling Errors

The difference between an estimated value and the population 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 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 that how close will the sample estimate be to the population true value. There are two kind of errors

1. Sampling Errors (random error)
2. Non sampling errors (non random errors)
1. Sampling Errors
A Sampling Errors is the difference between the value of a statistic obtained from an observed random sample and the value of corresponding population parameter being estimated.Let T be the sample statistic used to estimate the population parameter , the sampling error denoted by E  is  $E = T −θ$. The value of Sampling Errors reveals the precision of the estimate. 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

2. 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 error or non-random errors. These error are present in a complete census as well as in sampling survey.

Updated: Aug 5, 2015 — 6:14 pm