In statistical analysis, the quality of the outcome hinges on the integrity of the data. The data employed must be accurate and representative of all pertinent categories. While amassing more data enhances the impartiality of results, ensuring that the data gathered is relevant to the specific problem being addressed is significant.

One effective way to ascertain this relevance is understanding the distinction between population and sample. This tutorial will equip you with a comprehensive understanding of population versus sample.

The population denotes the entirety of individuals under consideration for conclusion. Conversely, a sample refers to the subset of individuals from which data will be collected.

Join The Fastest Growing Tech Industry Today!

Post Graduate Program In AI And Machine LearningExplore Program
Join The Fastest Growing Tech Industry Today!

What Is Population?

Population encompasses the complete set of individuals or items pique a researcher's interest in the study. This might encompass people, animals, plants, objects, or any other grouping. For instance, if a researcher aims to investigate the dietary patterns of all adults within a specific country, the population would consist of all adults residing in that country.

Ways of Collecting Data From a Population

Collecting data from an entire population can be challenging, particularly if the population is large or geographically dispersed. However, there are several methods researchers can use to gather data from a population:

  1. Census: Conducting a census involves collecting data from every individual or item in the population. This method provides the most accurate and comprehensive information but can be time-consuming, costly, and impractical for large populations.
  2. Administrative Data: Utilizing existing administrative records or databases maintained by government agencies, organizations, or institutions. These records often contain valuable information about individuals or items within a population, such as census data, tax records, healthcare records, or educational records.
  3. Surveys: They involve administering questionnaires or interviews to a representative population sample. While surveys are often used for sampling, they can also be employed to collect data from the entire population if feasible. This method allows researchers to gather information directly from individuals, providing insights into their opinions, behaviors, and characteristics.
  4. Direct Observation: Observing and recording information about individuals or items within the population firsthand. This method is commonly used in anthropology, ecology, and sociology, where researchers observe natural behaviors in their environments.
  5. Remote Sensing: Using remote sensing technologies such as satellites, drones, or sensors to collect data about environmental characteristics or phenomena within a population. Remote sensing is particularly useful for studying large geographic areas or inaccessible locations.
  6. Social Media and Web Data: Analyzing data generated from social media platforms, websites, or online communities to understand the behaviors, preferences, and interactions of individuals within the population. This method can provide valuable insights into digital populations and online communities.
  7. Physical Measurements: This method involves taking physical measurements or samples from individuals or items within the population. It is commonly used in biology, medicine, and engineering to collect objective physical characteristics or properties data.
  8. Ethnographic Research: Immersing oneself in the culture or community of interest to deeply understand the population's beliefs, practices, and social dynamics. Ethnographic research often involves prolonged engagement and participant observation.

Fast-track Your Career in AI & Machine Learning!

Post Graduate Program In AI And Machine LearningExplore Program
Fast-track Your Career in AI & Machine Learning!

When Is Data Collection From a Population Preferred?

  1. Accuracy is crucial: If you want to conclude the entire population with as much accuracy as possible, it's best to collect data from the entire population rather than just a sample. This is particularly important when the population is relatively small or when the characteristics of interest within the population are highly variable.
  2. Representativeness matters: When you need the sample to represent the entire population accurately, especially if there are subgroups within the population that you want to ensure are adequately represented.
  3. Resources allow: If resources such as time, money, and personnel permit, collecting data from the entire population can provide the most comprehensive insights.
  4. Unbiased analysis: Sometimes, researchers may want to avoid potential biases introduced by sampling methods. By collecting data from the entire population, they can eliminate sampling bias.

What Is a Sample?

A sample is a subset of individuals, items, or observations selected from a larger group or population to represent the characteristics of that larger group. In other words, it's a smaller, manageable portion of a population studied to make inferences about the whole population.

What Is Sampling and Why Is It Important?

Sampling is the process of selecting a representative subset of the population for study. It involves choosing individuals or items from the population using various techniques and methods. Sampling is crucial in research for several reasons:

  1. Practicality: It's often impractical or impossible to study an entire population due to time, cost, and logistics constraints. Sampling allows researchers to obtain meaningful insights from a smaller, more manageable population subset.
  2. Efficiency: Sampling enables researchers to collect data efficiently by focusing resources on a population subset rather than attempting to gather information from every individual or item. This can save time and resources while providing valuable information about the population.
  3. Generalizability: When done properly, sampling allows researchers to make valid inferences about the entire population based on the characteristics of the sample. By selecting a representative sample, researchers can generalize their findings to the larger population with a certain level of confidence.
  4. Accuracy: Sampling methods are crafted to mitigate bias and optimize the precision of findings. By employing randomization and other sampling techniques, researchers endeavor to secure a sample that faithfully mirrors the population, thereby mitigating the likelihood of skewed or erroneous outcomes.
  5. Ethical Considerations: In some cases, studying an entire population may be unethical or impractical, especially if the research involves sensitive topics or vulnerable populations. Sampling allows researchers to minimize potential harm and respect ethical guidelines while conducting valuable research.

Become an AI & ML Expert with Industry Specialists

Post Graduate Program In AI And Machine LearningExplore Program
Become an AI & ML Expert with Industry Specialists

Key Steps Involved in the Sampling Process

The sampling process comprises several crucial steps to guarantee the representativeness of the chosen sample about the population and the reliability of the collected data. Below, we outline the essential steps in the sampling process:

  1. Define the Population: Clearly define the population of interest the research aims to study. This could be a specific group of individuals, items, or observations with common characteristics.
  2. Determine the Sampling Frame: Identify the list or source from which the sample will be drawn. The sampling frame should include all elements of the population and should be accessible for sampling.
  3. Choose a Sampling Method: Based on the research objectives, population characteristics, and available resources, select an appropriate sampling method. Typical sampling techniques encompass random, stratified, cluster, and convenience sampling.
  4. Determine Sample Size: Determine the appropriate sample size needed to achieve the desired level of precision and confidence for the study. Sample size calculations often consider factors such as the population size, variability, and desired margin of error.
  5. Select the Sample: Use the chosen sampling method to select the sample from the sampling frame. Ensure that the sampling process is random or systematic to minimize bias and ensure representativeness.
  6. Obtain Informed Consent: If the research involves human subjects, obtain informed consent from participants before collecting data. Inform participants about the purpose of the study, their rights, and any potential risks or benefits involved.
  7. Collect Data: Once the sample is selected, collect data from the sampled individuals, items, or observations using appropriate data collection methods such as surveys, interviews, observations, or measurements.
  8. Analyze Data: Analyze the collected data using appropriate statistical techniques and methods. Ensure the analysis accounts for any sampling design or weighting factors to obtain accurate estimates and make valid inferences about the population.
  9. Interpret Findings: Interpret the study's findings in the context of the population and research objectives. Draw conclusions based on the analysis of the sample and consider any limitations or biases that may affect the generalizability of the results.
  10. Report Results: Communicate the study's results through written reports, presentations, or publications. Document the sampling methods, sample characteristics, data collection procedures, and findings to facilitate transparency and reproducibility.

Population vs Sample: Differences





The research aims to study the entire group of individuals, items, or observations.

A subset of individuals, items, or observations selected from the population for study.


Typically larger, comprising all elements of interest within a defined group.

Smaller, representing a portion of the population.


Consists of every member or element of the group.

Represents a proportion or subset of the population.


The target of generalization in research, where conclusions are drawn.

Used to make inferences about the larger population.

Data Collection

May involve various methods depending on the research goals and resources available.

Typically collected through sampling methods such as random sampling, stratified sampling, or convenience sampling.


Analysis aims to understand the entire group's characteristics, trends, and patterns.

Analysis focuses on making inferences about the population based on the characteristics of the sample.

Examples of Population

Here are some examples of populations:

  • All Adults in a Country: This population comprises every adult living within a specific country's borders.
  • All Students in a University: This population includes every student enrolled at a particular university, regardless of their field of study or year of enrollment.
  • All Employees in a Company: This population consists of all individuals who work for a specific company, including full-time, part-time, and contract employees.
  • All Registered Voters in a District: This population encompasses every registered voter within a defined electoral district or constituency.
  • All Patients Diagnosed with a Disease: This population comprises all individuals diagnosed with a specific medical condition or disease.
  • All Species in an Ecosystem: This population includes every plant, animal, and microorganism species within a particular ecosystem or habitat.
  • All Products Sold by a Retailer: This population consists of every product available for sale by a retail store or online retailer.
  • All Vehicles Registered in a City: This population encompasses every vehicle registered with the local transportation authority within a specific city or municipality.
  • All Houses in a Neighborhood: This population includes every residential dwelling within a defined neighborhood or community.
  • All Tweets on a Social Media Platform: This population comprises every tweet posted on a specific social media platform within a given timeframe.
Looking forward to a successful career in AI and Machine learning. Enrol in our Caltech Post Graduate Program in AI and ML in collaboration with Purdue University now.

Examples of Sample

  • Population: All Adults in a Country
    • Sample: A random selection of 1,000 adults chosen from a national database of citizens.
  • Population: All Students in a University
    • Sample: A stratified sample consisting of 200 undergraduate students and 100 graduate students randomly selected from the university's enrollment records.
  • Population: All Employees in a Company
    • Sample: A convenience sample of 50 employees volunteering to participate in a workplace satisfaction survey.
  • Population: All Registered Voters in a District
    • Sample: A systematic sample of every 10th registered voter from the electoral roll in the district.
  • Population: All Patients Diagnosed with a Disease
    • Sample: A purposive sample of 50 patients selected from a hospital's medical records based on the severity of their condition.
  • Population: All Species in an Ecosystem
    • Sample: A random sample of 20 quadrats placed across the ecosystem, with each quadrat surveyed for the presence of plant and animal species.
  • Population: All Products Sold by a Retailer
    • Sample: A simple random sample of 100 products randomly selected from the retailer's inventory.
  • Population: All Vehicles Registered in a City
    • Sample: A cluster sample of 10 randomly selected city blocks, with all vehicles parked within those blocks surveyed.
  • Population: All Houses in a Neighborhood
    • Sample: A systematic sample of every 5th house along a street within the neighborhood.
  • Population: All Tweets on a Social Media Platform
    • Sample: A stratified sample of 1,000 tweets, with 200 tweets randomly selected from five hashtags.


Understanding the distinction between population and sample is fundamental in research methodology across diverse fields. While the population encompasses all the individuals, items, or observations under study, the sample represents a subset chosen for analysis. Recognizing this disparity allows researchers to draw meaningful inferences about the larger group based on the characteristics of the sample.

To explore AI and ML, consider enrolling in the Caltech Post Graduate Program in AI and Machine Learning. This comprehensive program provides a structured curriculum designed by industry experts and academia, ensuring an in-depth understanding of cutting-edge AI and ML concepts.

Our AI & Machine Learning Courses Duration And Fees

AI & Machine Learning Courses typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Applied Generative AI Specialization

Cohort Starts: 31 May, 2024

4 Months$ 4,000
Post Graduate Program in AI and Machine Learning

Cohort Starts: 3 Jun, 2024

11 Months$ 4,300
AI & Machine Learning Bootcamp

Cohort Starts: 3 Jun, 2024

6 Months$ 10,000
Professional Certificate Program in No Code Machine Learning

Cohort Starts: 12 Jun, 2024

4 Months$ 2,565
Generative AI for Business Transformation

Cohort Starts: 21 Jun, 2024

4 Months$ 3,350
AI and Machine Learning Bootcamp - UT Dallas6 Months$ 8,000
Artificial Intelligence Engineer11 Months$ 1,449