Sample Vs. Population: What’s The Difference?

Understanding greater trends among a group of people (especially consumers) can be vital for progression. For instance, a company that wants to improve reception toward its products would benefit from understanding what consumers think of a said product.

However, collecting en masse requires surveys and other data collection. And knowing the difference between a sample and a population is crucial for effective data collection.

Luckily, to find out more about all of the important differences between a sample and a population, this article will dive into everything you need to know.

Key Takeaways:

What is a Population?

A population is an entire group or set of items you wish to draw conclusions from. Typically, wanting to understand more about a specific population is the reason for conducting a new study or survey.

Often, population refers to a group of people living in a specific place at a specific time. However, in the context of statistics, a population can refer to any group of interest. When conducting a study, a population can refer to anything from a species of animal to a line of products.

Examples of a population:

• All residents of a country

• All employees at a company

• All of the advertisements made by a company

• Every chicken on a farm

• Every student at a university

This diversity is the reason why the difficulty of studying a population can vary.

For instance, if a study seeks to understand a small tribe that consists of 15 people, that study would be able to collect data from the entire population. As long as all of the tribespeople are willing to participate in the study, the data set will be complete and reliable.

On the other hand, if someone wanted to do a study on the population of Ireland, drawing reliable conclusions from all 5 million+ people suddenly becomes illogical. Geographical and logistical constraints, as well as a lack of time, would all prevent the study from accessing every Irish person. In turn, this would lead to missing and unreliable data.

In this case, a sample would be necessary.

What Is a Sample?

A sample is a more manageable subsection of a larger group that can be studied. In turn, the data collected from the sample can be used to draw interesting and partially accurate conclusions about the population.

It’s essential for the sample to retain characteristics of that population, as the more closely the sample represents the population as a whole, the more accurate and unbiased the data will be.

Samples are important because they allow an otherwise impossibly large population to be studied. When surveying 1,000 people gives generalized information about 1 million people, organizations can save time, money, and manpower.

For example, here are some common reasons for using samples:

• Scale. The population is too large for accurate data collection, which means more useful information can be gathered from a smaller but still representative group.

• Inaccessibility. Portions of the population cannot be reached for accurate data collection or are simply hypothetical. Therefore, a sample is the best alternative for data collection.

• Practicality. It will be easier, more efficient, and less time-consuming to collect data from a sample.

• Cost-effectiveness. Often, there are far fewer participant, equipment, logistical, and research costs required when collecting data from a sample.

• Storage. Smaller data sets are easier to manage and, therefore, easier to draw accurate conclusions from.

With all of that in mind, here are some examples of samples as they related to a population:

• Residents of New York, Ohio, and Michigan surveyed on their opinions of Coca-Cola, with the intention of understanding the U.S. opinion of the drink.

• Data collected from 100 workers at a large company of over 1,000 employees meant to draw conclusions about workplace culture.

• The top 50 most popular advertisements made by a company in the month of June are meant to provide data on the success of all advertisements.

• A collection of 200 chickens from one of five farms meant to provide data on how common a new illness is.

• 500 undergraduate students from seven American colleges who chose to participate in a research study about the affordability of groceries for students.

Population Vs. Sample FAQ

1. When can data be collected from a population?

   Data can be collected from a population when every member of the population is available. While this availability typically becomes more limited in larger or more distant populations, a large population on its own doesn’t guarantee the impossibility of data collection.

   For example, if all 300 students in a school can be reached to give their opinions on school lunches, then data from the entire population can be collected.

   More realistically, however, it is much easier to collect data from 20 people than it is to collect it from 300 people. This is why samples often become necessary to draw conclusions about a larger population.

2. How do you collect accurate data from a sample?

   Accurate data can be collected from a sample when the sample is unbiased. To do this, samples should be randomly selected in a way that accurately represents the population. For example, probability sampling can be used to gather samples from every subsection within the population.

   Consider polling the popularity of certain candidates during an election. By randomly selecting groups of Americans from different ethnic, regional, and economic backgrounds, the data collected will be more valid and accurate than a single poll done in San Francisco, CA.

3. Can the sample and population be the same?

   No, the sample and the population cannot be the same. By definition, the sample will always be smaller in scale than the entire population. In fact, the population being too large or too difficult to gather accurate data from is what necessitates samples within research.

4. What is the difference between a statistic and a parameter?

   The difference is that statistic refers to measures describing the sample, while a parameter refers to measures describing the population. For example, the mean of the population would be considered a parameter, while the mean of a statistic would be a statistic.

   It is important to keep these terms distinct, as when differences between the two arise, it’s often an indication of sample bias.

5. Why would data from a sample be biased?

   Data from a sample would be biased if it wasn’t representative of the population as a whole. In other words, the sample statistic is different from the population parameter.

   For example, let’s say a gym wants to know if its new equipment appeals to all of its members. Only 25% of gym members are over 40, but 60% of sampled gym members were over 40. In this case, the results of the surveyed gym members are biased toward the opinions of older members.

   On the other hand, the sample would become less biased if the gym used probability sampling to email the survey to a more representative group of members.

   This is also commonly known as a sampling error.

Conclusion

Understanding the difference between a population and a sample is crucial for conducting accurate and viable research. After all, even though collecting data from an entire population is often ideal, it’s not always possible.

In summary, a population refers to the entirety of a group or set of items you wish to draw conclusions from, whereas a sample is a more manageable subsection of a larger group that can be studied.

While being able to study an entire population can give you the most accurate information, it’s often implausible due to the physical restraints (time, money, manpower, etc.). Therefore, a representative sample of the population can be used to collect valuable data instead.

Overall, deciding whether or not to study a population or a sample is largely dependent on what’s being studied and the capabilities of the researchers. In either case, it is vital to prioritize research that is accurate and unbiased.

Author

Jack Flynn

Jack Flynn is a writer for Zippia. In his professional career he’s written over 100 research papers, articles and blog posts. Some of his most popular published works include his writing about economic terms and research into job classifications. Jack received his BS from Hampshire College.