{"id":60028,"date":"2025-05-22T05:22:16","date_gmt":"2025-05-22T05:22:16","guid":{"rendered":"https:\/\/quillbot.com\/blog\/?p=60028"},"modified":"2025-11-26T08:37:02","modified_gmt":"2025-11-26T08:37:02","slug":"population-vs-sample","status":"publish","type":"post","link":"https:\/\/quillbot.com\/blog\/research\/population-vs-sample\/","title":{"rendered":"Population vs Sample | Definitions, Comparison & Formulas"},"content":{"rendered":"

In research, the terms population<\/strong> and sample<\/strong> describe who you are studying. A population<\/strong> is the entire group you care about. A sample<\/strong> is a smaller group of individuals who you actually collect data from to make inferences, or educated guesses, about the population.<\/p>\n

Population vs sample example<\/figcaption>Elections and election polls highlight the differences between<\/a> a population and a sample.<\/p>\n

Before an election, pollsters can\u2019t survey the whole population<\/strong> (every registered voter). Instead, they collect data from a sample<\/strong> (a small group from the population) to predict the election outcome.<\/p>\n

If the sample is representative, it can provide a good guess of how the population will vote. If it\u2019s biased (maybe only certain types of people respond to polls), the sample may give inaccurate results.<\/figure>\n

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Population and sample definitions<\/h2>\n

Population and sample both refer to who or what you are studying. However, they\u2019re not quite the same\u2014the population is every possible individual you could include in your study, whereas the sample is the smaller group you actually collect data from.<\/p>\n

Population<\/h3>\n

When you are conducting research, the population is all individuals or entities you care about. A population can be broad (e.g., all adult men in North America) or narrow (e.g., all geophysics majors currently enrolled at Stanford University). A population can also comprise nonhuman things, like all the products manufactured on an assembly line or wheat crops in Saskatchewan, Canada.<\/p>\n

Note<\/figcaption>Though population is commonly defined in terms of geography (e.g., the population of California), any number of other defining characteristics can be used. A population might be anyone who could benefit from a drug, all the users of an app, or English-speaking women aged 25\u201335 who work in data science.<\/p>\n

How you define your population will depend on who and what you want to study. Ensuring a representative sample of this population will help produce results that have external validity<\/a> and can be generalized.<\/figure>\n

Sample<\/h3>\n

The goal of collecting a sample is to make educated guesses (or inferences) about a population. Collecting data from every individual in a population is often impractical or impossible, so researchers generally collect data from a subgroup of individuals called a sample. This sample is meant to represent the population.<\/p>\n

How a researcher selects a sample from a population is a nontrivial decision. The researcher must balance practicality with how representative their sample is. If people with certain characteristics are more likely to be sampled than others, the sample may not reflect the population of interest\u2014this is known as sampling bias<\/strong>. Different sampling methods<\/strong><\/a> exist that vary in their convenience and susceptibility to sampling bias.<\/p>\n

Population vs sample example<\/figcaption>How the population and sample are defined depends entirely on one\u2019s research question<\/a>. Consider the difference in the following two scenarios:<\/p>\n

Scenario 1<\/strong>
\n<\/strong>A doctor wants to know if one of their patient\u2019s iron levels are low. They draw a small blood sample from their patient and analyze it to draw conclusions about all the blood in the patient\u2019s body.<\/p>\n