Sample and population in statistics

What is population?

Population means the total number of data you have .
Example:
If there is 1000 students and if you want to work on 1000 students then it is called population. This means we are working on every data we have.

What is sample?

Sample is a subset of the population.
Example:
If there are 1000 students and you have to do a survey. If you randomly pick 100 students from 1000 students then 100 students will be called sample. Because you picked 100 students from 1000(population) students. You can also say the sample is a subset of the population.

Most uses sampling techniques:

1. Random sampling technique:

A random sample is a subset of the population. Here each member of the subset has an equal probability of being selected.
Example:
There are 300 employees' resumes but you have to choose 50 randomly. To do this take all the resumes and put them in a box and then randomly start selecting. In this case, every employee has the same probability of being selected.

2. Systematic sampling technique:

Suppose there are 40 people and you have to select 8 people among them.
In the systematic technique, you will divide 40 by 8, after doing that you will get the result 5, it's mean that you have to select every 5th person from the total population.

3. Stratified sampling technique:

Here the population is divided into layers. Here we divide the whole population into smaller groups and these groups are known as strata.
Example:
There are 100 people but you have to select 25 among them. In this technique, you will create many smaller groups among the total population. depending on age, height, color, etc. Then select people(sample) from each group.

4. Cluster sampling technique:

Here you will divide the population into separate groups according to some means like school zone, residential area, commercial area, etc and these groups are called cluster. Then randomly choose some of these clusters.