 # Sample Vs Population

Before moving forward, let us understand these two terminologies which make the base for Statistics.

Population:

When we collect all the items of a particular type, it is considered as a population.

For. e.g.

• Set of weights of all the people in the world
• Set of all the countries in the world
• Set of all the colleges in the world
• Set of all the cells in a human body
• Set of all the hairs on my head
• Set of all the colors
• Set of all the customers of a bank
• Set of all the employees of a company etc.

Population contains everything of a particular type. First, we decide the scope/domain, and we consider everything from that domain.

If our analysis is only restricted to India, then India is our population, if our analysis is worldwide, then we need to consider all the countries for data collection.

Sample:

A sample is a part/subset of a population. Example: Properties of a Sample:

1. A Population can have multiple samples
2. A sample can have any size varying from 1 to n - 1 (where n is the no. of items present in the population)
3. Sample size can not be equal to Population size, otherwise, it will be called population not the sample
4. A sample can be biased (specific items) or unbiased (completely random)

Note: For point 4, we will discuss in detail what is biased and unbiased sample under-sampling techniques.

Why is Sample needed, why can’t we use Population itself?

A sample is considered because working on a population is difficult. Let us understand this with few examples:

1) What is the average length of hair on my head?

Is it possible to measure the length of all the hairs? Probably not!!

So, we consider some 100 - 1000 hairs and take their average.

2) What is the average weight of all the ants in my house?

Is it possible to collect all the ants and weigh them? Probably not!!

So, we will collect whatever we can, and take the average of their weights.

3) What is the temperature of the human body?

Is it possible to thermal screen everybody on earth? Probably not!!

So, we will measure the temperature of a few people groups from different parts of the globe and calculate the average.

The above situations are the cases where it is hard to find the population, therefore we collect a sample, work on it and infer/conclude about the population.

Note: Population mean will be slightly different from the sample mean. So above averages are the conclusions for a sample, but to get population conclusions, we may need to apply some formulas.

Bengaluru, India