A population is the entire group you want to draw conclusions about, while a sample is the smaller subset you actually collect data from. In research, you study the sample but use it to make inferences about the whole population, because measuring every single member is usually impractical, expensive, and time-consuming.
In research, the population includes everyone or everything relevant to your study, while the sample is a representative slice chosen from it. This guide explains the difference between a population and a sample, the types of population in research, how a parameter differs from a statistic, the main probability and non-probability sampling methods, and worked examples to help you choose the right approach for your dissertation or project.
What Is a Population in Research?
In research and statistics, a population is the entire group of people, items, or cases that a study is interested in. It is the full set about which you want to draw conclusions. The population does not have to mean people: it can be objects, events, organisations, or measurements, as long as the members share the characteristic you are studying. For example:
- If your research is about the study habits of UK university students, then all UK university students make up your population.
- If you are studying customer satisfaction at a retail chain, every customer who shops there is part of the population.
- In healthcare research, a population could be all patients diagnosed with a specific condition in a country.
“A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.” — Scribbr, Population vs Sample
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Key Characteristics of a Population
- It includes everyone or everything relevant to the research question
- It is defined by clear inclusion and exclusion criteria (the target population)
- It is often theoretical, meaning you may never actually reach every member
- A numerical summary of a population is called a parameter
Types of Population in Research
Not every population looks the same. Understanding the types of population in research helps you define your study scope accurately and choose a realistic sampling strategy. The main types are:
| Type of Population | What It Means | Example |
|---|---|---|
| Target population | The full group you ideally want to study and generalise to. | All nurses working in the UK. |
| Accessible population | The portion of the target population you can realistically reach. | Nurses in three NHS trusts that agreed to take part. |
| Finite population | A countable group with a fixed number of members. | All 18,000 students enrolled at one university. |
| Infinite (or hypothetical) population | A group so large it cannot be counted, or one that does not yet fully exist. | All future users of an online service; every possible coin toss. |
| Existent population | A population of concrete objects or people that physically exists. | All cars currently registered in London. |
| Hypothetical population | A population that is imagined or conceptual rather than physically present. | All possible outcomes of a repeated experiment. |
Why Studying the Whole Population Is Difficult
In an ideal world, researchers would study the whole population. But in reality, that is rarely possible. Populations can be:
- Too large to measure in full
- Too expensive to access
- Spread across different locations
- Time-consuming to reach
This is where samples come in.
What Is a Sample?
A sample is a smaller group selected from the population that is intended to represent the larger group. The number of members in the sample is the sample size, and it is always smaller than the total population size.
Instead of studying everyone, researchers study some members and use the findings to understand the whole population through inferential statistics. For example:
- Surveying 300 students out of 20,000 enrolled at a university
- Interviewing 50 patients from a hospital rather than all patients nationwide
- Analysing 200 online reviews instead of millions
Why Samples Are Used
Samples make research:
- Faster
- More affordable
- Easier to manage
- More realistic for students and academics
As long as the sample is chosen carefully, it can still provide reliable and meaningful results.
What Makes a Good Sample?
A good sample should:
- Represent the population fairly
- Be large enough to support conclusions
- Be selected using a clear, justified method
Avoid research bias as much as possible.
Population vs Sample: Key Differences
Although population and sample are closely linked, they are not the same. It is important to understand the difference so that you can explain your research design clearly. The table below summarises how they compare.
| Population | Sample | |
|---|---|---|
| Meaning | Entire group being studied | Smaller subset selected from it |
| Size | Large (sometimes infinite) | Smaller and manageable |
| Cost | High | Lower |
| Time Required | Long | Shorter |
| Feasibility | Often impractical | Practical and realistic |
| Numerical summary | Parameter (e.g. μ, σ, N) | Statistic (e.g. x̄, s, n) |
| Purpose | Ideal target of the research | Used to estimate and draw conclusions |
In short:
- Population = who you want to understand
- Sample = who you actually study
Parameter vs Statistic
A common point of confusion is the difference between a parameter and a statistic. The distinction mirrors population vs sample exactly:
- A parameter is a value that describes a population (for example, the true mean μ of all students). It is usually unknown.
- A statistic is a value that describes a sample (for example, the sample mean x̄). It is calculated from data and used to estimate the parameter.
Parameter vs statistic
- Describes the whole population
- Fixed but usually unknown
- Estimated from a sample
- Varies sample to sample
For a deeper breakdown of the symbols and formulas, see our guide on parameter and statistics.
| Measure | Parameter (population) | Statistic (sample) |
|---|---|---|
| Mean | μ (mu) | x̄ (x-bar) |
| Standard deviation | σ (sigma) | s |
| Proportion | P | p̂ |
| Size | N | n |
Why Researchers Use Samples Instead of Populations
If populations give the full picture, why don’t researchers always use them? The answer is simple: practical limitations.
1. Time Constraints
Most research projects, especially student research, have strict deadlines. Studying an entire population could take years.
2. Cost Issues
Surveys, interviews, lab tests, and fieldwork all cost money. Sampling keeps research affordable.
3. Accessibility Problems
Some population members may be:
- Difficult to contact
- Unwilling to participate
- Geographically scattered
4. Data Management
Handling data from thousands or millions of participants can be overwhelming. Samples make analysis manageable.
5. Ethical Considerations
In medical or psychological research, testing everyone may not be ethical or safe.
Because of these reasons, sampling is not a weakness. It is a smart and accepted research practice — even national bodies rely on it. The UK Office for National Statistics’ Labour Force Survey, for instance, samples around 35,000–40,000 households per quarter rather than the whole population to estimate employment for the entire country.
“The Labour Force Survey (LFS) is the largest household study in the UK… it interviews a sample of around 35,000 to 40,000 responding households in each quarter.” — UK Office for National Statistics (ONS)
Types of Sampling Methods
Sampling is not random guessing. Researchers follow specific methods to ensure fairness, accuracy, and credibility. Sampling methods are generally divided into two main categories:
- Probability sampling — every member has a known, non-zero chance of selection
- Non-probability sampling — selection depends on availability, judgement, or purpose
The summary table below compares both before we look at each method in detail.
| Probability Sampling | Non-Probability Sampling | |
|---|---|---|
| Selection | Random; known chance for each member | Non-random; based on access or judgement |
| Bias | Lower | Higher |
| Generalisable? | Yes, to the population | Limited |
| Common in | Quantitative research | Qualitative research |
| Main types | Simple random, stratified, systematic, cluster | Convenience, purposive, quota, snowball |
a. Probability Sampling
Probability sampling means that every member of the population has a known, non-zero chance of being selected. This type of sampling is common in quantitative research and is often preferred because it reduces bias and lets you generalise findings to the population. There are four common types.
1. Simple Random Sampling
This is the most straightforward method.
- Every individual has an equal chance of selection
- Selection is done randomly (like a lottery)
Example: Randomly selecting 100 student ID numbers from a university database.
| Pros of Simple Random Sampling | Cons of Simple Random Sampling |
|---|---|
| Fair and unbiased | Requires a complete population list |
| Easy to understand | Not always practical for large or scattered populations |
2. Stratified Sampling
The population is divided into groups (strata) based on shared characteristics such as gender, age, or course. Samples are then selected from each group, usually in proportion to the group’s size.
Example: Dividing students into undergraduate and postgraduate groups, then sampling from both.
| Pros of Stratified Sampling | Cons of Stratified Sampling |
|---|---|
| More representative of diverse populations | More complex to design |
| Allows comparison between subgroups | Requires knowing each member’s stratum in advance |
3. Systematic Sampling
Researchers select participants at regular intervals from an ordered list (for example, every kth member after a random start).
Example: Choosing every 10th name on a class list.
| Pros of Systematic Sampling | Cons of Systematic Sampling |
|---|---|
| Simple and quick to apply | Can be biased if the list follows a hidden pattern |
| Spreads the sample evenly across the list | Still needs an ordered list of the population |
4. Cluster Sampling
The population is divided into clusters, usually based on location. Whole clusters are then selected randomly, and all members within the chosen clusters are studied.
Example: Selecting 5 universities at random and surveying all students within them.
| Pros of Cluster Sampling | Cons of Cluster Sampling |
|---|---|
| Cost-effective for large, spread-out populations | Less precise than other probability methods |
| Useful for geographical studies | Risk of bias if clusters are not similar to each other |
b. Non-Probability Sampling
In non-probability sampling, not everyone has a known or equal chance of being selected. Selection depends on availability, judgement, or purpose. This method is commonly used in qualitative research, case studies, and student projects. There are four common types.
1. Convenience Sampling
Participants are selected because they are easy to access.
Example: Surveying classmates or friends.
| Pros of Convenience Sampling | Cons of Convenience Sampling |
|---|---|
| Quick and cheap | High risk of bias |
| Ideal for small projects | Limited generalisation |
2. Purposive Sampling
Participants are chosen because they meet specific criteria relevant to the research aim.
Example: Interviewing only final-year nursing students for a healthcare study.
| Pros of Purposive Sampling | Cons of Purposive Sampling |
|---|---|
| Focused and relevant | Subjective selection |
| Useful for in-depth research | Not statistically generalisable |
3. Quota Sampling
Researchers select participants to meet pre-set quotas for certain characteristics.
Example: Interviewing equal numbers of male and female respondents.
| Pros of Quota Sampling | Cons of Quota Sampling |
|---|---|
| Produces a balanced sample | Selection may still be biased |
| Faster than probability methods | Not random, so not generalisable |
4. Snowball Sampling
Existing participants help recruit others, building the sample like a rolling snowball. It is especially useful for hard-to-reach or hidden populations.
Example: Interviewing one freelancer who then refers you to others.
| Pros of Snowball Sampling | Cons of Snowball Sampling |
|---|---|
| Useful for hard-to-reach populations | Limited diversity |
| Effective in social research | Possible referral bias |
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