CONCEPT OF POPULATION

Concept of Population

Abstract

The concept of population in statistics refers to the complete set of individuals, items, or observations that possess at least one common characteristic of interest to the researcher. Understanding population is essential for accurate data collection, generalization, and statistical inference. This note explains the meaning, characteristics, types, and importance of population in statistics, along with relevant examples.

Keywords

Population, Sample, Universe, Parameter, Census, Variable, Data Collection, Population Types, Statistic

Population

In statistics, a population refers to the entire group about which information is desired. It may consist of people, objects, events, institutions, or measurements sharing a common feature. Since studying the whole population is often difficult, statistics uses sampling techniques to study a smaller group and generalize the results. A clear understanding of population helps in proper research design, sampling, and interpretation.

Definition of Population

A population includes the total number of units (individuals or items) that satisfy the conditions or characteristics specified by the researcher

Example: If you want to study the average height of adult women in India, the population would be all adult women in India

 Characteristics of Population

A population in statistics refers to the complete group of individuals or items that share common characteristics relevant to a study. Every population has certain key characteristics. First, it has a defined size, which may be finite or infinite depending on how many elements it contains. It also includes population parameters, such as the mean, variance, and proportion, which describe its numerical features (Triola, 2018). Another characteristic is variability, meaning the members of a population are not identical and may differ in their attributes (Utts & Heckard, 2015). Additionally, a population must have clear boundaries or criteria, ensuring that only units meeting specific conditions—such as time, place, or property—are included (Montgomery & Runger, 2014). These characteristics help researchers accurately define, analyse, and interpret the population under study.

Population Vs Sample

In statistics, a population refers to the complete set of individuals or observations that share a common characteristic the researcher aims to study. It includes every member of the group—for example, all students in a school or all citizens in a country (Triola, 2018). Because collecting data from an entire population is often impractical, researchers use a sample, which is a smaller subset selected from the population. A well-chosen sample represents the population closely, allowing researchers to make valid conclusions about the whole group using sample statistics (Gravetter&Wallnau,2017). The accuracy of generalizations made from a sample depends greatly on sampling methods, sample size, and the absence of bias. Therefore, understanding the relationship between population and sample is fundamental for designing effective research and interpreting data correctly (Triola, 2018). Population and sample are represented in Figure 1

Figure1

Population and sample

From the Figure 1 illustrates the concept of population and sample, where the larger group of individuals (population) is shown on the left, and a smaller group selected from it (sample) is shown on the right.

Parameters of a Population

Parameters of a population are numerical measures that describe the true characteristics of an entire population. These values represent real, fixed quantities such as the population mean (average), population proportion, population variance, population standard deviation, population median, and others. They provide a complete picture of the population’s nature—for example, the average income of all households in a country or the proportion of people who are left-handed in a city. Although population parameters are constant, they are usually unknown because studying every individual in a large population is impractical. Therefore, researchers use sample statistics to estimate these parameters. In essence, population parameters act as the true benchmarks that guide statistical analysis and help in making accurate inferences about the whole population. The various types of population are given below in Table 1

Table 1

Types of Population

Type of Population	Description	Examples
Finite Population	A population with a limited and countable number of units	A group of students in a school
Infinite Population	A population with unlimited or uncountable members	Outcomes of an unlimited number of coin tosses
Existent Population	The actual population existing at a given time and place	Residents of a city on a census date
Hypothetical Population	A constructed or theoretical population for study purposes	Simulated population models in research

From the Table 1 illustrates different types of population in statistics- Finite, Infinite, Existent, Hypothetical along with their descriptions and examples.

 Importance of Population in Statistics

In statistics, the concept of population is crucial because it defines the entire group of individuals, items, or events to which a researcher intends to generalize findings. A clearly identified population ensures that the research objectives are focused and that the sample selected truly represents the larger group. This improves the accuracy, reliability, and validity of statistical results. Defining the population also forms the foundation of statistical inference, allowing conclusions drawn from a sample to be applied to the whole population with known levels of uncertainty. Without a well-defined population, statistical analysis may lead to biased results and incorrect generalizations. Therefore, the population is essential for proper sampling, meaningful interpretation, and scientifically sound conclusions in statistical studies (Flick, 2018; Lohr, 2022).

Conclusion

Population is a fundamental concept in statistics as it represents the complete group about which the researcher seeks information. Understanding the nature, types, and characteristics of population ensures proper sampling, accurate data collection, and meaningful statistical analysis. A clear definition of population strengthens the credibility, accuracy, and relevance of research findings.

References

Gravetter F. J., & Wallanu, L. B. (2017). Statistics for the behavioural science (10th ed.). Cengage Learning.

Montgomery, D. C., & Runger, G. C. (2014). Applied statistics and probability for engineers (6th ed.). Wiley.

Triola, M. F. (2018). Elementary statistics (13th ed.). Pearson.

Utt’s, J. M., & Heckard, R. F. (2015). Mind on statistics (5th ed.). Cengage Learning.