Population and sample are two terms used in statistics to describe a group of data.

Population refers to the entire group of data from which one can draw conclusions. It is the complete set of observations from a given study. For example, if you wanted to study the population of a city, you would need to include all the people living in that city.

Sample, on the other hand, is a subset of the population. It is a smaller group of data chosen from the population to represent the population as a whole. Sampling is used to make inferences and predictions about the population. For example, if you wanted to study the population of a city, you could take a sample size of 100 people from that city and use that sample to make inferences and predictions about the population.

The main difference between population and sample is that population is the entire group of data from which one can draw conclusions, while a sample is a subset of the population used to represent the population as a whole. Population is typically much larger than a sample, and it is important to note that a sample can never be representative of the entire population.

Another key difference between population and sample is that population data is usually collected through census or survey methods, while sample data is typically collected through random sampling. Random sampling is a method of selecting a sample of people from a population in a way that gives each member of the population an equal chance of being chosen. This ensures that the sample is representative of the population as a whole.

In conclusion, population and sample are two terms used in statistics to describe a group of data. Population refers to the entire group of data from which one can draw conclusions, while sample is a subset of the population used to represent the population as a whole. Population data is usually collected through census or survey methods, while sample data is typically collected through random sampling.