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Population vs Sample — Differences and examples. While the population provides a comprehensive overview of the entire group under study, the sample, on the other hand, allows researchers to draw inferences and make generalizations about the population.
A population is the entire group that you want to draw conclusions about while a sample is the specific group that you will collect data from.
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. Example: Suppose you are conducting research on smartphone usage habits among teenagers in a specific city.
Population vs sample is a crucial distinction in statistics. Typically, researchers use samples to learn about populations. Let’s explore the differences between these concepts! Population: The whole group of people, items, or element of interest. Sample: A subset of the population that researchers select and include in their study.
Here is an example of a population vs. a sample in the three intro examples. Example 1: What is the median household income in Miami, Florida? The entire population might include 500,000 households, but we might only collect data on a sample of 2,000 total households.
In this guide, we'll be focusing on two main types: population and sample data. Population data consists of information collected from every individual in a particular population. Meanwhile, sample data consists of information taken from a subset—or sample—of the population.
Instead of collecting the entire population, you take a smaller group of the population, a snapshot of the population. This smaller group, called a sample, is a subset of the population, see Figure 1-1. Sample – a subset from the population. Consider the following three research questions: