Understanding Statistics: The Difference Between Population and Sample

Population

In the field of statistics, population refers to the entire set of individuals, objects, or events that we are interested in studying or analyzing

In the field of statistics, population refers to the entire set of individuals, objects, or events that we are interested in studying or analyzing. It can represent any group, ranging from people in a specific town, students in a school, or even all the cars in a particular country. The population is the complete collection of all possible members.

For example, let’s say you’re studying the height of all the students in your school. In this case, the population would consist of every student in your school, regardless of whether or not you measure their heights.

In statistics, it’s often impossible or impractical to study the entire population. Therefore, we typically work with a subset called the sample. A sample is a smaller group selected from the population to represent it. By studying the sample, we can make inferences and draw conclusions about the entire population.

To illustrate this, let’s say your school has 1,000 students, but you’re only able to measure the heights of 100 of them. In this case, the 100 students you measured would be considered your sample, and you would use the information gathered from the sample to make inferences about the heights of all 1,000 students in the school.

In summary, in statistics, the population is the complete set of individuals, objects, or events that we are interested in studying, while a sample is a smaller subset of the population that we analyze to make conclusions about the entire population.

More Answers: