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Difference between Population and Sample

When it comes to statistics, there is a lot of terminologies that can be confusing. Two important terms are population and sample. Though they sound similar, these two concepts are actually quite different. This blog post will explain the difference between population and sample, and provide examples to help make it clear. By understanding the difference between these two terms, you’ll be able to better understand statistical concepts and analyses.

What is Population?

Population type of data statistical testing is a method used to analyze a population. This method is used to study the characteristics of a population, and to make inferences about the population based on the analysis. Population type of data statistical testing can be used to study a variety of phenomena, including the distribution of a disease, the prevalence of a particular behavior, or the demographics of a population. Population type of data statistical testing is an important tool for understanding populations and for making decisions about how to best serve them.

What is a Sample?

Sample data is a set of data that is collected from a population and used to estimate the characteristics of that population. Statistical testing is a process of using statistical methods to make inferences about a population based on a sample. There are many different types of tests, but all involve making some kind of assumption about the distribution of the data in the population. The most common type of test is the t-test, which assumes that the data is normally distributed.

Other tests, such as the chi-square test, do not make this assumption. Sample data can be either quantitative or qualitative. Quantitative data is numerical and can be measured, while qualitative data is non-numerical and cannot be measured. Qualitative data can be categorized into two types: ordinal and nominal. Ordinal data can be ranked, while nominal data cannot.

Difference between Population and Sample

Population and sample are two types of data that are used in statistical testing. Population data is a set of all observations that can be made in a given situation. Sample data is a subset of the population data. It is a smaller set of observations that are used to represent the population. Population data is usually too large to be used for statistical testing, so sampling is used to select a smaller set of data that is representative of the population.

There are several different methods of sampling, including random sampling, stratified sampling, and systematic sampling. Population and sample data are both important for statistical testing, but they have different roles. Population data provides the complete picture, while sample data is used to represent the population and to test hypotheses.


The difference between population and sample is an important distinction to understand when designing surveys or experiments. A population is the entire group of people, animals, or things being studied, while a sample is a smaller portion of that population that is chosen for study. It’s important to remember that samples are not always representative of the whole population – they may be biased in some way, which can lead to inaccurate results. When you’re conducting research, it’s crucial to make sure you understand the difference between population and sample so that your data is accurate.

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