# Population and Sample in Data Science

• By Mahesh Kankrale
• June 27, 2024
• Data Science

# Population and Sample in Data Science

Understand the crucial concepts of Population and Sample in Data Science with SevenMentor Institute. Learn how to differentiate and utilize these key elements to enhance your data analysis skills.

## Population:

This refers to the entire collection of individuals or items we’re interested in studying. It encompasses all elements that share a specific characteristic.

For example:

1. The population could be all the students in a particular school district.
2. It could be all the registered voters in a country.
3. In medicine, it might represent all patients with a specific disease.

## Sample:

This is a subset of the population that you actually collect data from. It’s a manageable group chosen to represent the characteristics of the larger population.

For example :

•   Let’s say you want to understand the average height of college students in your country.
• The population would be all the college students in the country. This is likely too large a group to feasibly measure.
• Your sample could be a group of 100 students randomly chosen from different colleges across the country. By analyzing the heights of these 100 students (the sample), you can estimate the average height of the entire population (all college students).

## Sampling Techniques :

Sampling techniques are methods used to select a representative subset (sample) from a larger population. Choosing the right technique depends on your research question and the characteristics of the population you’re studying. Here’s a breakdown of some common sampling techniques:

### A. Probability Sampling:

• Involves random selection, allowing you to statistically estimate the population’s characteristics.
• Every member of the population has a known and equal chance of being included in the sample. This enables stronger statistical inferences about the whole group.

Simple Random Sampling:

• Each member of the population has an equal chance of being chosen. Think of picking names out of a hat!
• Requires a complete list of the population (sampling frame).

• The image depicts a bowl filled with colorful balls. Each ball represents an individual member of a population – it could be people in a town, students in a class, or any other group we want to study.

Systematic Sampling:

• Members are chosen at regular intervals from a list of the population.
• Good if the population is ordered in a way that reflects the characteristic you’re interested in.
• For example, if you want to survey every 10th customer entering a store, systematic sampling would be appropriate.

#### Stratified Sampling:

• The population is divided into subgroups (strata) based on relevant characteristics.
• A random sample is then selected from each subgroup proportionally to its size in the population.
• Ensures representation of different subgroups within the population.

Example:

Sampling households from different income brackets (e.g., low, middle, high) for consumer behavior surveys.

### B. Non-Probability Sampling:

Selection is not random, and it’s difficult to make statistical inferences about the population.

However, these methods can be quicker and easier to implement, especially when a random sample is impractical.

#### Convenience Sampling:

• The easiest and most accessible method. You select the most readily available individuals.
• May not be representative of the entire population, leading to biased results.