what is population in business research

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What Is Population?

Understanding populations, how to measure a population, population and investing, the bottom line.

• Fundamental Analysis

Population Definition in Statistics and How to Measure It

Pete Rathburn is a copy editor and fact-checker with expertise in economics and personal finance and over twenty years of experience in the classroom.

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In statistics, a population is the pool from which a sample is drawn for a study. Thus, any selection grouped by a common feature can be considered a population. A sample is a statistically significant portion of a population.

Key Takeaways

• In statistics, a population is the entire group on which data is being gathered and analyzed.
• It is generally difficult in terms of cost and time to gather the data needed on an entire population, so samples are often used to make inferences about a population.
• A sample of a population must be randomly selected for the results of the study to accurately reflect the whole.

Statisticians , scientists, and analysts prefer to know the characteristics of every entity in a population to draw the most precise conclusions possible. However, this is impossible or impractical most of the time since population sets tend to be quite large. A sample of a population must usually be taken since the characteristics of every individual in a population cannot be measured due to constraints of time, resources, and accessibility.

It's important to note that when referring to an individual in statistics, the term does not always mean a person. Statistically, an individual is a single entity in the group being studied.

For example, there is no real way to gather data on all of the great white sharks in the ocean (a population) because finding and tagging each one isn't feasible. So, marine biologists tag the great whites they can (a sample) and begin collecting information on them to make inferences about the entire population of great whites. This is a random sampling approach because the initial encounters with tagged great whites are entirely random.

A valid statistic may be drawn from either a sample or a study of an entire population. The objective of a random sample is to avoid bias in the results. A sample is random if every member of the whole population has an equal chance to be selected to participate.

The difficulty of measuring a population lies in whatever you're attempting to analyze and what you're trying to accomplish. Data must be collected through surveys, measurements, observation, or other methods.

Therefore, gathering the data on a large population is generally not done because of the costs, time, and resources necessary to obtain it. For instance, when you see advertisements claiming, "62% of doctors recommend XYZ for their patients,"—all of the doctors with patients who could use XYZ in the U.S. were likely not contacted. Of the doctors who responded to the several hundred or thousand surveys that were requested, 62% responded that they would recommend XYZ—this is a population sample.

While a parameter is a characteristic of a population, a statistic is a characteristic of a sample, and samples can only result in inferences about a population characteristic. Inferential statistics enables you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population.

Statistics such as  averages  (means) and  standard deviations , when taken from populations, are referred to as population parameters. Many, such as a population's mean and standard deviation, are represented by Greek letters like µ (mu) and σ (sigma). Much of the time, these statistics are inferential in nature because samples are used rather than populations.

If you have all the data for the population being studied, you do not need to use statistical inference because you won't need to use a sample of the population.

Market and investment analysts use statistics to analyze investment data and make inferences about the market, a specific investment, or an index. In some cases, financial analysts can evaluate an entire population because price data has been recorded for decades. For example, the price of every publicly traded stock could be analyzed for a total market evaluation because the prices are recorded—this is a population, in terms of investment analysis. Another population might be the stock prices of all tech companies since 2010.

An analyst can calculate parameters with all of this data; however, the parameters used by analysts are only occasionally used in the same way statisticians and scientists use them.

Some of the parameters you might see used by investment analysts, statisticians, and scientists and their differences are:

Alpha : The excess returns of an asset compared to a benchmark

Standard Deviation : Average amount of variability in prices, used to measure volatility and risk

Moving Average : Used to smooth out short-term price fluctuations to indicate trends

Beta : Measures the performance of an investment/portfolio against the market as a whole

Alpha : The probability of making a Type I error, or rejecting the null hypothesis when it is true

Standard Deviation : Average amount of variability in data

Moving Average : Smooths out short-term fluctuations in data values

Beta : The probability of making a Type II error, or incorrectly failing to reject the null hypothesis

What Is the Population Mean?

A population mean is the average of whatever value you're measuring in a given population.

What Are 2 Examples of Population?

One example of a population might be all green-eyed children in the U.S. under age 12. Another could be all the great white sharks in the ocean.

What Is the Best Example of a Population?

Imagine you're a teacher trying to see how well your fifth-grade math class did on a standardized test compared to all fifth-graders in the U.S. The population would be all fifth-grade math scores in the country.

In statistics, a population is the pool being studied from which data is extracted. Populations can be difficult to gather data on, especially if the studied topic is expansive and widely dispersed. Studying humans is an excellent example—there is no way to gather data on every brown-eyed person in the world (a statistical population), so random sampling is the only way to infer anything about that population.

In investment analysis, populations are generally specific types of assets being analyzed. These data sets are generally small (in statistical terms) and easy to acquire because they have been recorded, unlike data on living organisms, which is much more difficult to obtain.

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Data Collection Methods

Population vs sample in research: What’s the difference?

Population and sample are two important terms in research. Having a thorough understanding of these terms is important if you want to conduct effective research — and that’s especially true for new researchers. If you need a primer on population vs sample, this article covers everything you need to know, including how to collect data from either group.

What is a population?

Outside the research field, population refers to the number of people living in a place at a particular time. In research, however, a population is a well-defined group of people or items that share the same characteristics. It’s the group that a researcher is interested in studying.

Arvind Sharma , an assistant professor at Boston College, explains that a population isn’t limited to people: “It can be any unit from which you obtain data to carry out your research.” This group could consist of humans, animals, or objects.

Below are some examples of population:

• Male adults in the United States
• World Cup football matches
• Insects in American rainforests

As you can see from the examples above, populations are usually large, so it’s often difficult to survey an entire population. That’s where sampling comes in.

What is a sample?

A sample is a select group of individuals from the research population. A sample is only a subset or a subgroup of the population and, by definition, is always smaller than the population. However, well-selected samples accurately represent the entire population.

Below are some examples to illustrate the differences between population vs sample:

The sample a researcher choses from any population will depend on their research goals and objectives. For example, if you’re researching employees in a large corporation, you may be interested in C-level executives, junior-level employees, or even external contractors.

What are the differences between population and sample?

Below are the main differences between a population and a sample, as pointed out by Sharma:

What are some reasons for sampling?

Collecting data from an entire population isn’t always possible. “In fact,” explains Sharma, “99 percent of the time, we can’t survey the entire population. Other times, it is not even necessary.

“A representative sample drawn using appropriate sampling techniques will provide results that are representative of the entire population. So, it would be unnecessary to survey every member of the population.”

Below are the other most important reasons for using sampling.

Population studies are more expensive than sample surveys. For example, researching the entire population of adult male Americans would be too costly. It’s more cost-effective to work with a representative sample.

2. Practicality

Consider the adult male American research example. Even if a researcher had the resources to survey all the males in that population, it may be difficult or impossible to obtain responses from all participants. For example, the researchers may not even be able to contact all members of this population.

3. Manageability

It’s easier to manage time, costs, and resources when working with samples. Also, it’s easier to manage the data you collect from a sample vs a population. For example, it’s easier to analyze data from a sample of 1,000 adult males than a sample of all adult males in the U.S. or even a specific state.

How can you collect data from a population?

Collecting data from an entire population requires a census. A census is a collection of information from all sections of the population. It’s a complete enumeration of the population, and it requires considerable resources, which is why researchers often work with a sample.

If the target population is small, however, then you can collect data from every member of the population. For example, you can survey the performance of the members of the customer service team in a bank branch. The number is likely to be more manageable, so you can access and collect data from this population.

What methods can you use to collect data from a sample?

There are so many approaches for collecting data from samples. Some of the more commonly used methods are listed below.

1. Simple random sampling

In simple random sampling, researchers select individuals at random from the population. In this method, every member of the population has an equal chance of being selected.

For example, suppose you want to select a sample of 50 employees from a population of 500 employees. You could write down all the names of the employees, place them in a hat or container, and pick employee names at random like you would in a lottery. That’s an example of simple random sampling. It works best when the population isn’t too large.

2. Systematic sampling

This is a sampling technique that selects every k th item from the population. It’s a type of probability sampling researchers use to select items from a population randomly. A researcher may want to use this technique if they’re working with a large population and need to sample only a small number of items in order to study them in detail.

For example, to apply systematic sampling in a performance survey of 1,000 customer service team members, we can choose every fifth member — i.e., the fifth, 10th, 15th customer service rep, and so on.

For more details on  what is systematic sampling , check out our guide

3. Stratified sampling

In this probability sampling method, researchers divide members of the population into groups based on age, race, ethnicity, or sex. Researchers select individuals randomly from those groups to form a sample. This ensures that every group is equally represented.

What is a sampling error?

A sampling error is the difference between the value obtained from a sample and the true population value. It’s the difference between an estimate from a sample and the true population value.

A sampling error can occur if you don’t have enough people in your sample or if you select people who aren’t representative of the population. This can impact the accuracy of your survey. For example, if you want to know what percentage of adults are vegetarian but only ask vegetarians in a specific city, then this would be an example of selecting people who aren’t representative of the population.

According to Sharma, you can reduce sampling errors by increasing the sample size . He also notes that sample design and variation within a population affect sampling errors.

How can Jotform make the research process easier?

Whether you’re surveying a small or large sample or even an entire population, Jotform gives you the right tools to make your research easier. With Jotform’s free online survey maker, you can create engaging surveys and collect responses online. You can easily customize any of our 10,000-plus free survey templates to suit your research purposes. Get started with Jotform today.

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• What is target population: Definition & examples

Researchers should consider many factors for the research to be successful when conducting research. One of the essential steps is identifying the target population for the research study early on while planning a market research study and thinking about goals and objectives . 

A target population sets a clear direction on the scope and object of the research and data types. This article will explain what the target population is in a research example and Frequently ask questions about the target population with all the details. 

• What is the target population in research?

The population that the intervention is intended to study and take conclusions from is known as the target population. A target population, also referred to as a target audience , is a group of people with particular characteristics that may be effectively defined to distinguish them from the general population. 

The target population aims to comprehend and assess their preferences and behaviors to promote a particular good or service or to research a specific feature that frequently manifests itself in their behavior, such as behavior patterns. It is a notion that has to do with business market segmentation tactics. 

A target population is typically a group or collection of factors you want to learn more about. The target population is a subset of the general public identified as the targeted market for a given product, advertising, or research. It is a subset of the entire population chosen to serve as the objective audience.

• How to determine the target population in a research design

Identifying the target population requires careful preparation , precise research questions, and a robust study design . It will assist you in obtaining trustworthy and accurate information that responds to your study issue or concern.

The target group is frequently chosen based on characteristics or demographics such as age , gender , employment , income , or health condition . The research's conclusions are then extrapolated to the broader population from whom the target sample was selected. 

How to choose your target population

Narrowing objectives is one of the most sensible things for businesses to do when identifying a target market so that their goods or services may be promoted effectively. The quality of the service will rise, and controllability will be significantly aided by an attempt to offer a more specific target population. 

First, you should define the target population you wish to attract with your marketing campaigns to create a target audience. The following steps will assist you in determining the population. 

• Set business or brand goals: This is essential in creating a content strategy or developing a marketing plan. 
• Define your study’s objective: You should clearly state your study's motivation and objective.
• Determine the population features: Determine the features of your intended audience. Gender, age, educational background, socio-economic situation, and career should be determined.
• Market research: Market research is the process of collecting and analyzing data about the market in which a business operates to understand the target audience and the potential of a specific product or service. 
• 3 target population examples

Target population determination is an ongoing process that requires research, analysis, and adjustments. You can identify the specific group of people you want to reach with your marketing efforts and create a strategy to interact with them effectively. Here are 3 target population examples. 

• Determining target population according to specific features: Let's say you are researching school service fees. First, you should consider specific characteristics when determining the target population. As parents whose children go to school and parents who send their children to school by service, you can narrow down your target audience in your research. 
• Determining target population according to characteristic features: Another example is you are launching a new perfume as a cosmetics company. Your further perfume appeals to young women. You should determine the age group of the target population . Then you focus on gender , and maybe you can consider the women using perfume. You can choose your target population in this way. 
• Determining target population according to target audience: The last example is that you are a business manager and want to measure the customer's satisfaction with a product. The target population would be all the customers of a particular business. 
• Frequently asked questions about the target population

The target population is a big group of people that researchers are looking at. The target population is very similar to other population types. There are frequently asked questions about the target population.

What is the target population in quantitative research

In quantitative research, the term "target population" refers to the group of people or things the researcher wishes to analyze and draw conclusions about based on the data collected.

What is the target population in qualitative research

In qualitative research, the term "target population" refers to the group of participants or subjects who are of interest to the researcher to explore, comprehend, and examine subjective experiences, behaviors, attitudes, or occurrences. Audiences often choose The target group based on criteria like gender, age, education, and other features.

Target population vs. sample population

The difference between the sample population and the target population is that the target population is a more significant subset of the target population chosen for the research project. In contrast, the sample population is the entire group of people or things the researcher is interested in working with.

Target population vs. accessible population

The difference between the target and accessible population is that an accessible population is that portion of the target population that may theoretically be included in the study. In contrast, a target population is the whole set of instances the researcher desires to analyze.

Target population vs. sampling frame

The difference between the target population and the sampling frame is that the sampling frame is a subset of the target population. The sampling frame typically serves as the starting point for the sampling process. The sample is a portion of the population and the individuals or items you have access to. In contrast, the target population is the entire group of people or objects to which you desire to generalize the results of your study.

Target population vs. sample

The difference between the population and the sample is the target population is much larger than the sample. The target population is the entire group or items the researcher intends to examine and draw conclusions about. On the other hand, a sample is a subset of the target population that is selected for study.

In conclusion, a researcher's target population is the group they are trying to comprehend. When the target audience is analyzed, new information may be found that enables the business to launch various advertising campaigns tailored to the target population's income levels and attitudes. 

This article has explained the definition of the target population, How to determine the target population in a research design, and examples of the target population. When you read this article, you can learn all details about the target population.

Sena is a content writer at forms.app. She likes to read and write articles on different topics. Sena also likes to learn about different cultures and travel. She likes to study and learn different languages. Her specialty is linguistics, surveys, survey questions, and sampling methods.

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• Population vs Sample | Definitions, Differences & Examples

Population vs Sample | Definitions, Differences & Examples

Published on 3 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organisations, countries, species, or organisms.

Table of contents

Collecting data from a population, collecting data from a sample, population parameter vs sample statistic, practice questions: populations vs samples, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalised and low-income groups have been difficult to contact, locate, and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

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When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods . Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalise findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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Population and samples: the complete guide.

9 min read What are the differences between populations and samples? In this guide, we’ll discuss the two, as well as how to use them effectively in your research.

When we hear the term population, the first thing that comes to mind is a large group of people.

In market research, however, a population is an entire group that you want to draw conclusions about and possesses a standard parameter that is consistent throughout the group.

It’s important to note that a population doesn’t always refer to people, it can mean anything you want to study: objects, organisations, animals, chemicals and so on.

For example, all the countries in the world are an example of a population — or even the number of males in the UK. The size of the population can vary according to the target entities in question and the scope of the research.

When do you need to collect data from a population?

You use populations when your research calls for or requires you to collect data from every member of the population. Note: it’s normally far easier to collect data from whole populations when they’re small and accessible.

For larger and more diverse populations, on the other hand — e.g. a regional study on people living in Europe — while you would get findings representative of the entire population (as they’re all included in the study), it would take a considerable amount of time.

It’s in these instances that you use sampling. It allows you to make more precise inferences about the population as a whole, and streamline your research project. They’re typically used when population sizes are too large to include all possible members or inferences.

Let’s talk about samples.

What is a sample?

In statistical methods, a sample consists of a smaller group of entities, which are taken from the entire population. This creates a subset group that is easier to manage and has the characteristics of the larger population.

This smaller subset is then surveyed to gain information and data. The sample should reflect the population as a whole, without any bias towards a specific attribute or characteristic. In this way, researchers can ensure their results are representative and statistically significant.

To remove unconscious selection bias, a researcher may choose to randomise the selection of the sample.

Types of samples

There are two categories of sampling generally used – probability sampling and non-probability sampling :

• Probability sampling , also known as random sampling, is a kind of sample selection where randomisation is used instead of deliberate choice.
• Non-probability sampling techniques involve the researcher deliberately picking items or individuals for the sample based on their research goals or knowledge

These two sampling techniques have several methods:

Probability sampling types include:

• Simple random sampling Every element in the population has an equal chance of being selected as part of the sample. Find out more about simple random sampling.
• Systematic sampling Also known as systematic clustering, in this method, random selection only applies to the first item chosen. A rule then applies so that every nth item or person after that is picked. Find out more about systematic sampling .
• Stratified random sampling Sampling uses random selection within predefined groups. Find out more about stratified random sampling .
• Cluster sampling Groups rather than individual units of the target population are selected at random.

Non-probability sampling types include:

• Convenience sampling People or elements in a sample are selected based on their availability.
• Quota sampling The sample is formed according to certain groups or criteria.
• Purposive sampling Also known as judgmental sampling. The sample is formed by the researcher consciously choosing entities, based on the survey goals.
• Snowball sampling Also known as referral sampling. The sample is formed by sample participants recruiting connections.

Find out more about sampling methods with our ultimate guide to sampling methods and best practices

Calculating sample size

Worried about sample sizes? You can also use our sample size calculator to determine how many responses you need to be confident in your data.

Go to sample size calculator

When to use sampling

As mentioned, sampling is useful for dealing with population data that is too large to process as a whole or is inaccessible. Sampling also helps to keep costs down and reduce time to insight.

Advantages of using sampling to collect data

• Provide researchers with a representative view of the population through the sample subset.
• The researcher has flexibility and control over what kind of sample they want to make, depending on their needs and the goals of the research.
• Reduces the volume of data, helping to save time.
• With proper methods, researchers can achieve a higher level of accuracy
• Researchers can get detailed information on a population with a smaller amount of resource
• Significantly cheaper than other methods
• Allows for deeper study of some aspects of data — rather than asking 15 questions to every individual, it’s better to use 50 questions on a representative sample

Disadvantages of using sampling to collect data

• Researcher bias can affect the quality and accuracy of results
• Sampling studies require well-trained experts
• Even with good survey design, there’s no way to eliminate sampling errors entirely
• People in the sample may refuse to respond
• Probability sampling methods can be less representative in favour of random allocation.
• Improper selection of sampling techniques can affect the entire process negatively

How can you use sampling in business?

Depending on the nature of your study and the conclusions you wish to draw, you’ll have to select an appropriate sampling method as mentioned above. That said, here are a few examples of how you can use sampling techniques in business.

Creating a new product

If you’re looking to create a new product line, you may want to do panel interviews or surveys with a representative sample for the new market. By showing your product or concept to a sample that represents your target audience (population), you ensure that the feedback you receive is more reflective of how that customer segment will feel.

Average employee performance

If you wanted to understand the average employee performance for a specific group, you could use a random sample from a team or department (population). As every person in the department has a chance of being selected, you’ll have a truly random — yet representative sample. From the data collected, you can make inferences about the team/department’s average performance.

Store feedback

Let’s say you want to collect feedback from customers who are shopping or have just finished shopping at your store. To do this, you could use convenience sampling. It’s fast, affordable and done at a point of convenience. You can use this to get a quick gauge of how people feel about your store’s shopping experience — but it won’t represent the true views of all your customers.

Manage your population and sample data easily

Whatever the sample size of your target audience, there are several things to consider:

• How can you save time in conducting the research?
• How do you analyse and compare all the responses?
• How can you track and chase non-respondents easily?
• How can you translate the data into a usable presentation format?
• How can you share this easily?

These questions can make the task of supporting internal teams and management difficult.

This is where the Qualtrics CoreXM technology solution can help you progress through research with ease.

It includes:

• Advanced AI and machine learning tools to easily analyse data from open-text responses and data, giving you actionable insights at scale.
• Intuitive drag-and-drop survey building with powerful logic, 100+ question types, and pre-built survey templates . For more information on how to get started on your survey creation, visit our complete guide on creating a survey.
• Stylish, accessible and easy-to-understand reporting that automatically updates in real time, so everyone in your organisation has the latest insights at their fingertips.
• Powerful automation to get up and running quickly with out-of-the-box workflows, including guided setup and proactive recommendations to help you connect with other teams and react fast to changes.

Also, the Qualtrics online research panels and samples help you to:

• Choose a target audience and get access to a representative sample
• Boost the accuracy of your research with a sample methodology that’s 47% more consistent than standard sampling methods
• Get dedicated support at every stage, from launching your survey to reporting on the results.

Want to learn more?

Related resources

Market intelligence tools 10 min read, qualitative research questions 11 min read, primary vs secondary research 14 min read, business research methods 12 min read, ethnographic research 11 min read, business research 10 min read, qualitative research design 12 min read, request demo.

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Table of Contents

What is population, ways of collecting data from a population, when is data collection from a population preferred, what is a sample, what is sampling and why is it important, key steps involved in the sampling process, population vs sample: differences, examples of population, examples of sample, population vs sample: definitions, differences, and examples.

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.

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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.

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:

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

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• 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.
• 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.
• Resources allow: If resources such as time, money, and personnel permit, collecting data from the entire population can provide the most comprehensive insights.
• 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.

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.

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:

• 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.
• 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.
• 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.
• 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.
• 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.

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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:

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

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.

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• 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.

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Explaining Adverse Cholesterol Levels and Distinct Gender Patterns in an Indonesian Population Compared with the U.S.

In order to shed light on the biological and social drivers underlying the dramatic rise in cardiovascular disease risk in lower-income settings, links between these risks and body composition, behavioral and socioeconomic factors in Aceh, Indonesia, are contrasted with the United States. We focus on rigorously-validated measures of HDL and non-HDL cholesterol among adults. Indonesians present with adverse cholesterol biomarkers relative to Americans, despite being younger and having lower body mass index. Adjusting for age, these gaps increase in magnitude. Body composition, behaviors, demographic and socioeconomic characteristics that affect cholesterol do not explain between-country HDL differences, but do explain non-HDL differences, after accounting for medication use. On average, gender differences are inconsistent across the two countries and persist after controlling observed characteristics. Leveraging the richness of the Indonesian data to draw comparisons between males and females within the same household, the gender gaps among Indonesians are not explained for HDL cholesterol, but attenuated substantially for non-HDL cholesterol. This finding suggests that unmeasured household resources play an important role in determining non-HDL cholesterol. More generally, they appear to be affected by social and biological forces in complex ways that differ across countries and potentially operate differently for HDL and non-HDL biomarkers.

Discussions with Eileen Crimmins have been very helpful. This work was supported by the National Institute on Aging (grant numbers R01AG031266, R01AG065395, and T32AG51108), the Eunice Kennedy Shriver National Institute of Child Health and Development (grant number R01HD052762) and the Wellcome Trust (grant number OPOH 106853/A/15/Z). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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