In research, two important terms are population and sample. For new researchers, a thorough understanding of these terms is essential for conducting effective research. Suppose you want an introduction to population vs sample. In that case, this blog covers all you want to be aware of, including how to gather information from one or the other gathering.
What is the population in market research?
Population in research is a complete set of elements with a standard parameter between them. We are all aware of what the word ‘population’ refers as daily. Frequently it is used to describe the human population or the total number of people living in a geographic area of our country or state.
The ‘population’ in research doesn’t necessarily have to be human. It can be any parameter of data that possesses a common trait.
Example: The total number of ‘Pet’ Stores on Sunset Boulevard in Los Angeles, California.
Ways of collecting data from a population
Gathering information from a whole population requires a census. Census is an assortment or collection of data from all segments of the population. It’s a finished identification of the populace and requires extensive assets, which is the reason specialists frequently work with an example.
However, you can collect data from every member of the population if the target population parameters are small.
For Example, conduct a performance evaluation of the bank branch’s customer service representatives. The number is probably going to be more reasonable, so you can access and gather information from this population.
What is a sample in market research?
A sample is a smaller part of the whole, i.e., a subset of the entire population. It is representative of the population in a study. When conducting surveys, the representative sample is the members of the population who are invited to participate in the survey.
Hence said, a sample is a subgroup or subset within the population. This sample can be studied to investigate the characteristics or behavior of the entire population data. Subgroup analysis is crucial for tailoring treatments to specific patient groups, optimizing healthcare outcomes.
Data samples are created using various research methods like probability sampling and non-probability sampling. Sampling methods vary according to research types, based on the kind of inquiry and the quality of information required. And sampling error is harmful to sample data collection.
Example: A cat food company would like to know all the pet stores where it can sell its canned fish. The company has population data on the total number of pet stores on Sunset Boulevard.
This pet food manufacturer can now create an online research sample by only selecting the pet stores that sell cat food. The data characteristics are studied. The results are displayed in statistics and reports analyzed for business insights. Using data from the sample, the company can uncover ways to grow its business into the total population of pet stores.
Here are the most common sampling techniques
Sampling techniques are broadly classified into two types:
Probability sampling and non-probability sampling.
01. Probability sampling
Samples were chosen based on probability theory.
02. Non-probability sampling
Samples were chosen based on the researcher’s subjective judgment.
How to choose high-quality samples
Although we ensure that all the population members have an equal chance to be included in the sample, it does not mean that the samples derived from a particular population satisfying the criterion will be alike. They will still vary from one another. This variation can be slight or substantial.
For example, a set of samples of healthy people’s body temperatures will show less difference. But the difference in these people’s systolic blood pressure would be sizeable.
It is also observed that the data’s accuracy depends on the sample size. The accuracy is much lesser with a smaller sample size than using a larger study sample. Thus, if two, three, or more samples are derived from a population, the bigger they are, the more they resemble each other.
Population vs Sample – top seven reasons to choose a sample from a given population
Sampling is a must to conduct any research study. Here are the top seven reasons to use a sample:
Practicality
In most cases, a population can be too large to collect accurate data – which is not practical. Samples allow researchers to collect data that can be analyzed to provide insights into the entire population. Samples offer a representation of the whole population if sampled accordingly.Offers urgent data
When it comes to research, the amount of time available can be a defining factor for a study. A sample provides a smaller set of the population for review, delivering data useful to represent the whole population. Surveying a smaller sample, as opposed to the entire population, can save precious time for researchers and offer urgent data.Cost-effective
The cost of conducting research is often a parameter for the study. Researchers must do their best with the resources they have to conduct a survey and gain accurate insights. Surveying a representative population sample is cost-effective as it requires fewer resources – like computers, researchers, interviewers, servers, and data collection centers.Accuracy of representation
Depending on the sampling method, research conducted on a sample can be accurate with lesser non-response bias than if performed by the census. A sample that is selected using the non-probability method is an accurate representation of the population. This data collected can be used to gather insight into the whole community.
Inferential statistics
Inferential statistics is a process by which representative data is used to infer insights about the entire population. Inferential statistics can only be obtained using data samples, and such statistical methods are there as well. Data collected from a sample represents the whole population.A sample is more accurate than a census
A census of an entire population only sometimes offers accurate data due to errors such as inconsistent responses or non-response bias. A carefully obtained sample, however, does away with this sampling bias and provides more accurate data – that adequately represents the population.Manageable
Sometimes, collecting an entire data population is near impossible as some populations are too challenging to come by. In this case, a sample can represent the study as it is feasible, manageable, and accessible.
Population vs Sample – What is the difference?
The concept of population vs sample is important for every researcher to comprehend.
Understanding the difference between a given population and a sample is easy. You must remember one fundamental law of statistics: A sample is always a smaller group (subset) within the population.
In market research and statistics, every study has an essential inquiry at hand. Observation and experiment of a population sample size determine this inquiry’s result. It is done to derive insights that explain a phenomenon within the whole study population.
Usually, a population sample is used in research, as it is easier and cost-effective to process a smaller subset of the population rather than the entire group.
In this table, we can take a closer look at the difference between the sample and population:
|
Population |
Sample |
| The measurable characteristic of the population, like the mean or standard deviation, is known as the parameter. | The measurable characteristic of the sample is called a statistic. |
| Population data is a whole and complete set. | The sample is a subset of the population that is derived using sampling. |
| A survey of an entire population is accurate and more precise with no margin of error except human inaccuracy in responses. However, this may only sometimes be possible. | A population sample survey bears accurate results only after further factoring in the margin of error and confidence interval. |
| The parameter of the population is a numerical or measurable element that defines the system of the set. | The statistic is the descriptive component of the sample found by using sample mean or sample proportion. |
Conclusion
Although Population and Sample are two different terms, they both are related to each other. The population is used to draw samples. Making statistical inferences about the population is the primary purpose of the sample. Without the population, samples can’t exist. The better the quality of the sample, the higher the level of accuracy of generalization.
When using QuestionPro or any other survey tool for research, knowing the difference between population vs sample is essential. A sample is a subset of the population that is actually studied, while a population is the entire group of people or things that a researcher wants to study.
While planning a review utilizing QuestionPro, it is critical to painstakingly consider the objective populace and select a delegate test that precisely mirrors the qualities of the bigger populace. By doing this, researchers can ensure that their survey results are accurate and can be applied to the population in question.
Understanding the fundamental standards of examining and populace can assist specialists with keeping away from normal predispositions and entanglements that can think twice about the precision of their examination discoveries.
When combined with a solid understanding of population and sampling, using QuestionPro can produce high-quality research results that can be used to make well-informed choices in a variety of fields.
Right sampling is essential to conduct insightful market research. Explore quality samples with QuestionPro Audience.
