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)

## Introduction

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.

## Definitions

- 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.

## 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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