Have you ever wondered why election polls are so right (or so wrong)? The answer is almost always in the same place: in how the sample was chosen. And that, although it seems like a minor technical detail, can completely change the conclusions of any research. If you keep reading, you will understand exactly what a population and a sample are, what the difference between them is, and above all, why a poor choice can ruin data in which you have invested time and money.
What is a population in statistics?
In statistics, the population (also called the statistical universe) is the complete set of elements about which you want to obtain information. It doesn't have to be people: it can be a group of products, transactions, companies, or any other unit that shares a common characteristic.
Examples of population:
- All Spanish consumers over 18 who buy online.
- All employees of a multinational company.
- All cars manufactured in a production plant during a month.
The population size is the total number of elements that make up that group. It can be finite (for example, the 500 employees of a company) or practically infinite (all social media users in the world).
What is a sample?
The sample is a part or subset of that population. It is the group on which you actually conduct the study, with the intention of extrapolating the results to the entire set.
Golden rule: A sample is always smaller than the population from which it comes.
Simple example: If you want to know the satisfaction of the customers of a bank with 2 million users, you cannot ask all 2 million. You select a sample of, say, 1,500 people. You analyze their responses and, if the sample is representative, the results reflect the opinion of the total.
The sample size matters a lot: the larger it is (within limits), the smaller the margin of error and the more reliable your conclusions will be.
Difference between population and sample: comparison table
| Concept | Population | Sample |
| Definition | Total set of elements | Subset of the population |
| Size | Large or very large | Manageable and limited |
| Cost | Very high | Reduced |
| Time | Long | Short |
| Result | Parameter (exact data) | Statistic (estimation) |
| Example | All voters in Spain | 2,000 surveyed people |
The difference between population and sample is, in essence, a matter of scale and practicality. Studying the entire population is called a census; studying a part is called sampling.
Why not always study the entire population?
It seems like the perfect solution: if you ask everyone, the data is exact. But in practice, three obstacles prevent it:
- Economic cost: Reaching every individual in a population of millions requires resources that few organizations have.
- Time: A census can take months or years. Business decisions cannot wait.
- Accessibility: It is not always possible to contact all members of a population. Some are inaccessible or simply do not respond.
That is why well-executed sampling is the most powerful tool in research: it allows you to obtain valid conclusions with a fraction of the effort.
What is a representative sample (and why it's the most important thing)
Here is the core of the matter. A sample can be large and still be poorly designed. The classic error: in 1936, the magazine Literary Digest surveyed 2.4 million Americans to predict the presidential elections and failed miserably. The reason? Their sample was biased towards people with phones and cars, who did not represent the average voter of the time.
A representative sample must:
- Reflect the diversity of the population (age, gender, income level, region, etc.).
- Be large enough for the results to be statistically significant.
- Be selected using a rigorous method, not out of convenience.
Types of sampling: probability and non-probability
Once the difference between population and sample is clear, the next step is to choose how to select that sample. There are two main families:
Probability sampling
Every individual in the population has the same probability of being selected. It is the most rigorous method and allows for statistical inference with a calculable margin of error.
Main subtypes:
- Simple random: Each person has an equal chance and is drawn at random.
- Systematic: Selected from a list with fixed intervals (e.g., every tenth person).
- Stratified: The population is divided into groups (strata) and proportional samples are taken from each.
- Cluster: Entire geographic or natural groups are selected.
Non-probability sampling
Not all individuals have the same probability of being chosen. It is faster and cheaper but introduces biases and does not allow for precise statistical generalization.
Main subtypes:
- Convenience: Choosing whoever is closest at hand (student volunteers, in-store customers, etc.).
- Quota: Fixed proportions of certain groups are established (e.g., 50% men, 50% women).
- Snowball: Each participant recommends the next, useful for hard-to-reach populations.
In online research using consumer panels, quota sampling is the most widespread method because it allows building samples very similar to the target population without needing full random access.
How sample selection affects the quality of your data
A poorly chosen sample produces data that, even in large quantities, is useless for decision-making. The main problems are:
- Selection bias: When certain profiles are more likely to appear in the sample than others.
- Underrepresentation: Important groups of the population that are left out of the sample.
- Insufficient size: A margin of error so large that the results are inconclusive.
On the other hand, a well-designed sample—even if modest in size—generates data you can trust to launch a product, adjust a campaign, or make strategic decisions.
Conclusion
Understanding the difference between population and sample is not an academic exercise: it is the foundation of any reliable market research. The population defines the universe you are interested in studying; the sample is the group you actually work with. The bridge between the two—the sampling method—determines whether your data has real value or if it is simply numbers giving a false sense of certainty.
Before launching any survey or study, ask yourself these three questions: Have I well-defined my target population? Does my sample accurately represent it? Is the sample size sufficient for the level of error I can accept?
Answering these three questions correctly is the first step to obtaining data you can truly trust.
FAQ about population and samples
What is the difference between a parameter and a statistic?
A parameter is a value that describes the entire population (for example, the true average satisfaction of all your customers). A statistic is the equivalent value calculated from the sample. The statistic is an estimation of the parameter; the better the sample, the closer the statistic will be to the true parameter.
How is the appropriate sample size calculated?
It depends on three factors: the population size, the desired confidence level (usually 95%), and the acceptable margin of error (usually ±3% or ±5%). There are sample size calculators that automate this process. As a reference, for a very large population, a sample of between 385 and 1,067 people is usually sufficient for margins of error of 5% and 3% respectively, with a 95% confidence level.
Can a small sample be representative?
Yes, as long as it is well-selected. Size matters, but representativeness matters more. A well-stratified sample of 400 people can outperform a poorly designed sample of 4,000. What makes a sample useful is not just its volume, but how it reflects the diversity of the population you want to study.
