In the previous example, we spoke about calculating the mean height of 1,000 people out of the 10 million people living in New Delhi. While gathering the data of these 10 million people, let's say we started from a particular age or community, or in any sequential manner. Now, if we take 1,000 people who are consecutive in the dataset, there is a high probability that they would have similarities among them. This similarity would not give us the actual highlight of the dataset that we are trying to achieve. So, taking a small chunk of consecutive data points from the dataset wouldn't give us the insight that we want to gain. To overcome this, we use sampling.
Sampling is a technique to randomly select data from the given dataset such that they are not related to each other, and therefore we can generalize the results that we generate on this selected data over the complete dataset. Sampling is done over a population.
