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.
