Population vs Sample – the difference

The concept of population vs sample is an important one, 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 sample of the population determine the result of this inquiry. It is done to derive insights that explain a phenomenon within the whole population.

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What is the ‘population’ in market research?

Definition: Population in research is a complete set of elements that possess a standard parameter between them.

We are all aware of what the word ‘population’ means in our everyday life. 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.

What is a sample in market research?

Definition:  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 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.

Samples of data 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.

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 as two types:
Probability sampling and non-probability sampling.

  1. Probability sampling – Samples chosen based on the theory of probability.
    a. Simple random sampling
    b. Cluster sampling
    c. Systematic sampling
    d. Stratified random sampling
  2. Non-Probability sampling – Samples chosen based on the researcher’s subjective judgment.
    a. Convenience sampling
    b. Judgemental or Purposive sampling
    c. Snowball sampling
    d. Quota sampling

How to choose high-quality samples:

Although we make sure that all the members of a population have an equal chance to be included in the sample, it does not mean that the samples derived from a particular population and 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 temperature will show a very less difference. But the difference in these people’s systolic blood pressure would be sizeable.

It is also observed that the accuracy of the data depends on the size of the sample. The accuracy is much lesser with a smaller sample size compared to using a larger sample for the study. Thus, if two, three or more samples are derived from a population, the bigger they are, the more they tend to 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 offer a representation of the whole population if sampled accordingly. Samples allow researchers to collect data that can be analyzed to provide insights into the entire population.
  • It 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, that delivers data that is 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 the best with the resources they have at hand, to carry out a survey and gain accurate insights. Surveying a representative sample of a population is cost-effective as it requires fewer resources – like computers, researchers, interviewers, servers, and data collection centers.
  • Accuracy of representation: Depending on the method of sampling, 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. Data collected from a sample represents the whole population. Inferential statistics can only be obtained using data samples.
  • At times, a sample is more accurate than a census: A census of an entire population does not always offer accurate data due to errors such as inconsistency in responses, or non-response bias. A carefully obtained sample, however, does away with this bias and provides more accurate data – that adequately represents the population.
  • Manageable: Sometimes, collecting an entire population of data is near impossible as some populations are too challenging to come by. In this case, a sample can be used to represent the study as it is feasible, manageable, and accessible.

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Population vs Sample – What is the difference?

Usually, a sample of the population 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 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 done of an entire population is accurate and more precise with no margin of error except human inaccuracy in responses. However, this may not be
possible always.
A survey done using a sample of the population bears accurate results, only after further factoring 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.  

Although Population and Sample are two different terms, they both are related to each other. The population is used to draw samples. To make 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.

Right sampling is essential to conduct insightful market research. Explore quality samples with QuestionPro Audience.