Today let’s look at the topic population and sample.

Population refers to the collection of all the elements in a dataset. It is usually a large number and hence the processing of data of the whole population is a tedious task.

Population data is hard to observe and to deal with. So we use what is called a sample for the collection of data. Time and resources required are minimised to a great extent when we use sample.

So we consider a small part of the population as our dataset. This small portion of the population is called a sample.

Here we can observe that a sample is a subset of the population.

For example, if we have 10 lakh population in a state and we want to find the income details of the population, it is a very laborious job.

Hence we select around 1000 people randomly out of the population which becomes the sample set in this case.

Since we pick up the sample randomly, the details obtained from the sample will not vary much and will be approximately equal to that of the whole population. Therefore we choose sample rather than the population in most of the cases.

The real-life examples where sampling is used are exit polls during an election, recording feedback about a particular product.