Difference between Sample and Population

In probability and statistics, the terms population and sample are used quite frequently. However, due to the similarity of their definitions, it is quite common to confuse them. On the one hand, a population is the total of the elements to be studied. If the population is too large or there is some major situation that prevents the entire population from being studied, a sample is chosen. A sample acts as a representative of the population.

Comparison table
	Population	Sample
Definition	Also called “universe”. It is the set of elements or individuals on which observations and studies are carried out. In a few words, it is the grouping of all the elements that will be studied or analyzed.	On the other hand, a sample occurs when a population is too large to be analyzed in its entirety. In those cases, a sample of the population is selected to which the studies or analyzes that were intended for the population will be applied. It is a subset of members of a population.
Characteristics	The population is a random variable or magnitude. Within statistics, the population that is chosen usually has certain demographic characteristics or common characteristics. There are finite populations and infinite populations (this is rather an artificial concept).	A sample is always part of a population. The process of selecting a sample must be biased to prevent the phenomenon to be analyzed from being adequately represented. Sampling is considered to be more accurate than studying the entire population. This is because less data is handled and therefore there is a smaller margin of error.
Purpose	Collect enough data to analyze them and then draw conclusions.	Evaluate and analyze phenomena and characteristics of a population.