There are certain vital techniques which must be understood well if we want to understand the subject of statistics and measurements. Sampling is one such very important topic, which we’ll be covering in a couple of articles.

## Some terminologies first…

To understand the concept, we should also understand a few frequent terms.

__Element__– an object on which a measurement is taken__Population__– a**collection of elements**about which we wish to make an inference__Sampling units__*–***non-overlapping collections**of**elements**from the population that**cover**the entire population__Sampling frame__– a list of sampling units__Sample-__a collection of sampling units drawn from a sampling frame__Parameter__: numerical characteristic of a**population**__Statistic__: numerical characteristic of a**sample**

## What is Sampling?

The activity in which elements, from a population, are collected so as to represent the population. In this video, a very good introduction to sampling has been provided.

## Why Sampling?

Most of the times, the population is too large to measure all of its elements, thus sampling is done. A sample reflects the characteristics of the population from which it is drawn. *For example*, a machine produces 1000 spindles a day. It may be difficult to measure all of them, so we take samples and measure them.

It is very crucial that samples are selected carefully. Incorrect sampling may lead to incorrect inferences about the population.

Sampling has many advantages over exhaustive sampling, which covers the whole population.

- Sampling can save money
- Sampling can save time
- In case of destructive inspection, it is not prudent to do exhaustive sampling

In our next article, we’ll learn about the common sampling techniques.

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