The Topic Sampling Error is Relevant For UGC NET Commerce & Management as Follows:


Sampling Error occurs solely as a result of using a sample from a population rather than conducting a census of the population. It refers to the difference between a sample statistics used to estimate a population parameter and the actual but unknown value of parameter. Sampling errors are inherent and unavoidable.


  • Sampling: The process of selecting a part of the population.
  • Population: The entire group of people of interest from whom the researcher needs to obtain information.
  • Sample: A selected part of the population is called sample. It should be a true representative of the whole population.
  • Sample size: The number of people in the selected sample is known as sample size.
  • Statistics: Any value calculated from sample is called statistics.
  • Parameter: Any value calculated from population is called parameter.
  • Sampling frame: It means the list of individual or people included in the same. It reflects who will be included in the sample.
  • Sampling technique: It refers to technique or procedure used to select the members of the sample.

Errors in Sampling

Error: Statistical error is the difference between a value obtained from a data collection process and the true value for the population.

Data can be affected by two types of error:

  1. Sampling error

  2. Non-Sampling error

Sampling Error

How Sampling Error occurs ?

It can occur when:

  • The proportions of different characteristics within the sample are not similar to the proportions of the characteristics of whole population.
  • The sample is too small to accurately represent the population.
  • The sampling method is not random.
  • Sampling error are absent in a complete enumeration survey.

How Sampling Error can be reduced?

  • Unbiased sampling: Sampling error can be measured and controlled in random samples where each unit has a chance of selection, and that chance can be calculated.
  • Increase the sample size: Increasing the sample size will reduce the sample error.
  • Cross check: Biased responses should be avoided
  • Example: If we are taking a sample of men & women and we know that 51 % of the total   population are women & 49% are men, then we should aim to have similar populations in our   sample.
  • Appropriate sampling design: Specific sampling plan should be formulated and suitable sampling   procedure should be adopted.

Practice Question

The difference between a statistic and parameter is called

A. Probability

B. Sampling error

C. Random

D. Non-random.

Solution: B

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