The Topic Sampling Error is Relevant For UGC NET Commerce & Management as Follows:
- UGC NET Commerce (Unit- V Business Statistics & Research)
- UGC NET Management (Unit -VIII Statistics & Production Management)
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
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.
The difference between a statistic and parameter is called
B. Sampling error
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