## 3.1.14.1 Sampling errors

 3.1.14.1 Sampling errors

Even if utmost care has been taken in selecting a sample, the results derived from a sample study may not be exactly equal to the true value in the population. The reason is that estimates of are based on a part of population and not on the whole and samples are seldom, if ever, perfect miniature of the population. Hence sampling gives rise to certain errors known as sampling errors (or sampling fluctuations). These errors would not be present in a complete enumeration survey. However, the errors can be controlled. The modern sampling theory helps in designing the survey in such a manner that the sampling errors can be made small.

Sampling errors are two types: biased and unbiased

1. Biased errors: These errors arise from any bias in selection, estimation, etc. For example, if in place of simple random sampling, deliberate sampling has been used in a particular case some bias is introduced in the result and hence such errors are called biased sampling errors.

2. Unbiased errors. These errors arise due to chance differences between the members of population included in the sample and those not included. An error in statistics is the difference between the value of a statistic and that of the corresponding parameter.

Thus the total sampling error is made up of errors due to bias, if any, and the random sampling error. The essence of bias is that it forms a constant component of error that does not decrease in a large population as the number in the sample increased. Such error is, therefore, also known as cumulative or non compensating error. The random sampling error, on the other hand, decreases on an average as the size of the sample increases. Such error is, therefore, also known as non-cumulative or compensating error.