Difference Between Population And Sample
The article discusses the concept of population and sample in statistics and the difference between population and sample.
Data are alphanumeric representations of statistical variables, both quantitative and qualitative. The data are those elements that allow statistical inference to be made to know the causes and make the predictions. With the data, the two concepts we will explain to you are closely related: Sample and population.
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What is a Population?
The statistical population, also known as the universe, is the set or totality of elements to be studied.
A population’s elements comprise each associated individual because they share some common characteristics.
The statistical population can be real people, places, or things—for example, the adolescents of a town or the possible uses of sugar in cooking recipes. As it is challenging to carry out a study with all the elements that make up a population, especially if it is considered an infinite population, a representative sample is taken from to carry out the studies.
Types of populations
The population can be classified according to the number of individuals that make it up:
- Finite population: A finite population can be counted, and the members can be studied more easily. For example, the number of people enrolled in a gym.
- Infinite population: They are huge populations where it is tough to count its members, so only a portion of it is usually taken into account when conducting a study, thus selecting a sample. For example, the number of grains of sand on a beach.
- Real population: Real populations are groups of tangible members. For example, the number of animals in a zoo.
- Hypothetical population: They are viable populations that can be studied during an eventuality. For example, the number of premature births.
Also Read – Difference Between Probability and Non Probability Sampling
What is a Sample?
The sample is a representative part of a population whose elements share common or similar characteristics. It is used to study the population more feasibly because it can be easily counted. Samples are usually taken when a study is carried out on a specific population’s behavior, properties, or tastes.
These studies that are carried out on the samples serve to create norms or guidelines that will allow taking action or simply knowing more about the studied population. Sampling is a research tool that, when used properly, allows specific conclusions to be drawn and avoids biased results.
The main advantage of using the samples is the reduction of costs since it reduces the elements to be studied and can be carried out in less time. The most important factors when making a sample are representativeness, so the elements have common qualities depending on the purpose and randomness when selecting the elements to avoid a flawed sample.
Sample types
There are different types of techniques to form a sample.
- Random Sampling – It is a technique that offers the same possibility to the elements of being selected because they are taken at random. The types of random sampling are:
- Simple Random Sampling – Items are chosen from a list at random. Simple Random Sampling works most effectively when the universe is small and homogeneous.
- Systematic sampling: In systematic sampling, the first element is chosen randomly, and then the remaining elements are chosen at constant intervals.
- Stratified sampling: It is carried out by dividing the population into parts or strata that respond to established characteristics, and then the individuals to be studied are randomly chosen.
- Sampling by a conglomerate: In this case, the population is divided into heterogeneous groups, subdivided into homogeneous groups with common characteristics.
Related – Probability Distributions used in Data Science
Difference Between Population And Sample
Population | Sample | |
Definition | Universe of elements to be studied. | Selection of a part of the population. |
Features | It can be classified according to the number of individuals that make it up. | It is part of the population: it should comprise between 5% and 10% to be most effective. |
Variables | It has statistical variables. | Variable could be random. |
Objectives | To analyze the data collected regarding the common characteristics shared by the elements for various purposes. | To study the behavior, characteristics, tastes, or properties of a representative part of the population. |
Examples | The people who inhabit a country.The number of cars in a city.Students of a university. | Study the performance of students from five universities in a city in a specific subject. 500 students are randomly taken as a sample (100 from each institution) studying at the same level so that the sample is representative. |
Examples of Difference Between Sample And Population
Here are some examples to help you understand the differences between sample and population:
- To know the degree of professional satisfaction of the employees within the Coca-Cola company, the population would be all the company employees worldwide, and the sample could be the set of a selection of 10 employees from each country.
- Now we want to know the favorite vehicle brand for drivers ages 40 – 50 in Noida. The population is all the drivers of the mentioned ages who reside in Noida, and the sample would be a random selection of all residents with a driver’s license and that age.
- We want to know how many liters of water the children of a school drink per day. The population will be all the school’s students, and the sample can be the selection of a boy and a girl from every class.
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FAQs
Why do we use samples instead of studying the entire population?
Studying an entire population can be impractical, time-consuming, and expensive. Samples provide a more manageable way to gather information that can still reflect the characteristics of the population.
How is data collected from a population different from data collected from a sample?
Data collected from a population covers all group members, while data from a sample only represents a portion of the group. This can affect the accuracy and precision of the findings.
Can a sample perfectly represent a population?
While a well-designed sample can provide accurate insights, there's always a chance of sampling error, which means the sample might not perfectly reflect the population due to random variability.
What is the purpose of inferential statistics WRT populations and samples?
Inferential statistics allow researchers to make predictions and draw conclusions about a population based on sample analysis. It helps generalize findings from the sample to the larger group.
How do population and sample affect the generalizability of research findings?
The more representative the sample is of the population, the more confidently research findings can be generalized. A larger and more diverse sample typically leads to stronger generalizability.
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