When dealing with the statistical data, it is necessary to distinguish between the population and the sample.

**Defining Sample and the Population **

**Population – **Entire data or the members of the specified group. The complete study of the entire population is known as the census.**Sample** – The sample can be defined as the small part from the population in other words it is the subset taken from the population.

Usually, the sample is always less than the size of the population.

**Example:**

Population: All the patients in the hospital

Sample: Only some people in the hospital

In mathematical formulas, the population is usually denoted as n or N and the sample is denoted as n-1.

**Terminologies Related to Sample and the Population**

Target Population: It is the entire group of the population to be studied.

Sampling Unit: A smaller subunit from the sample is called a sampling unit.

Sampling Scheme: The method used for selecting the sampling units is called a sampling scheme.

**Formulae Related to Population and the Sample**

**Types of Samples **

Sample can be broadly classified into two types; they are probability sample and the non-probability sample.

Non-probability samples are further classified into,

- Convenience
- Judgmental
- Quota
- Snowball

Probability samples are further classified into,

- Simple Random
- Stratified
- Cluster
- Systematic

**Procedure to Select the Sample from the Population**

Generally, the method of random sampling has many types,

- Simple Random Sampling
- Stratified Sampling
- Cluster Sampling
- Systematic Sampling

**Simple Random Sampling**

A process of simple random sampling is a procedure for selecting the samples from the entire population. The benefit of this method is that it provides some pathway to analyze the statistical data of the result. Each sample in the population has an equal and independent chance of being selected.

**Stratified Sampling**

They are often defined as the identified subgroups in the defined population.

**Cluster Sampling**

The process of randomly selecting groups in the defined population.

The process of concluding the population from the characteristic study made from the sample is called inferential statistics.

**Why one should need a sample?**

- The group of the population is too large.
- In other words, it can be explained as it is unlimited in size.
- The entire population cannot be easily reached.
- Besides everything, it takes less time in studying and provides more accuracy.

Finally, if we say it in a sentence, the sample is the smaller group that takes part in the study and the population is the larger group for whom the results will apply.

**Reference**

https://stattrek.com/sampling/populations-and-samples.aspx

https://www.statsandr.com/blog/what-is-the-difference-between-population-and-sample/