Population vs Sample – Definitions, differences, and examples

Population	Sample
All university students in the UK (the entire group of interest).	200 students selected from 10 UK universities (a subset of the population).
All customers of a national bank (the total pool).	500 customers surveyed from three major branches (a representation of the customers).
All employees of a multinational company (the entire workforce).	150 employees from the marketing and finance departments (a smaller, targeted group).
All households in a city (every unit in the target area).	250 households chosen randomly for a housing survey (a measured portion).
All patients with diabetes in a country (the complete patient group).	300 patients receiving treatment in five hospitals (a manageable subset for study).

What Is A Population In Research

A population refers to the complete group of individuals, items, or data that a researcher wants to study or draw conclusions about. It includes every element that fits the criteria of the research question.

The population is the entire set from which data could potentially be collected.

A research population has several key features:

Size	It can be large (e.g., all university students in the UK) or small (e.g., all teachers in a single school), representing the total number of units of interest.
Scope	It defines the boundaries of who or what is included, based on factors such as age, location, occupation, or behaviour (the criteria for belonging).
Inclusivity	Every individual or element that meets the defined criteria is considered part of the population; it is the entire set from which a sample is drawn.

Types Of Populations

Researchers generally divide populations into two main categories:

Target Population

This refers to the entire group that the researcher aims to understand or draw conclusions about.

For instance, if a study focuses on higher education trends, the target population might be all university students in the UK.

Accessible Population

This is the portion of the target population that the researcher can actually reach or collect data from.

For example, if only students from 10 universities participate, that group represents the accessible population.

Population Example

Imagine a study investigating the impact of online learning on academic performance.

The population could be all university students in the UK.

However, since it’s impossible to survey every student, researchers often select a smaller group, a sample, to represent this larger population accurately.

What Is A Sample In Research

A sample is a smaller group selected from a larger population to take part in a research study. It represents the characteristics of the entire population, and allows researchers to draw conclusions without studying everyone.

A sample is a subset of the population that helps make research more manageable and efficient.

Researchers use samples because studying an entire population is often time-consuming, expensive, and impractical. Sampling allows them to:

Collect data quickly and efficiently
Reduce research costs
Focus on quality data collection and analysis
Make generalisations about the whole population with a reasonable degree of accuracy

Types Of Samples

There are two main categories of sampling methods, each serving a specific research need:

Probability Samples

Every individual in the population has a known chance of being selected. This method reduces bias and increases representativeness.

Random Sampling	Each member of the population has an equal chance of being selected. This is often achieved using random number generators.
Stratified Sampling	The population is divided into subgroups (strata) based on a characteristic (e.g., gender, age), and samples are randomly taken from each group to ensure proportional representation.
Cluster Sampling	The population is divided into clusters (e.g., schools, cities), and entire clusters are randomly selected for the study. All members within the chosen clusters are typically surveyed.

Non-Probability Samples

Selection is based on convenience or judgment rather than randomisation. This is often used in exploratory or qualitative studies.

Convenience Sampling	Participants are chosen simply because they are easily accessible and available to the researcher (e.g., surveying students in your own class).
Purposive Sampling	Participants are deliberately selected based on specific, pre-defined characteristics relevant to the study’s research question (e.g., interviewing only managers with 10+ years of experience).
Quota Sampling	The researcher ensures that the sample includes specific proportions of subgroups (e.g., 50% male, 50% female) to mirror the population, but selection within those groups is non-random.

Sample Example

For instance, if the population includes all university students in the UK, the sample might be 200 students selected from ten different universities to participate in a survey about online learning.

How to calculate population sample size in research?

To calculate sample size, researchers use statistical formulas that consider:

Population size (total number of individuals)
Confidence level (usually 95%)
Margin of error (commonly 5%)
Expected variability or response rate

A commonly used formula is:

Where:

n = sample size
N = population size
Z = Z-score (1.96 for 95% confidence)
p = estimated proportion (0.5 if unknown)
e = margin of error

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