**Descriptive Statistics** is the term given to the analysis of the data, which will show meaningful insights, patterns present in data. However this doesn’t allow us to make any conclusions beyond the given data points. Let us take an example, Suppose in a company if Higher Management asked for Revenue data. Then directly giving him Raw data will not make sense, but if Analyst does some Descriptive Analysis and present the output in proper way to his higher management then they will be able to understand that more properly with many more information which remains hidden inside the data.

Another type of Statistics is **Inferential**.

But before defining this let’s define **Sample** and **Population** first.

A **population** is a collection of people, items, or events about which we want to know, study and make inferences.

It is not always possible to study whole population as it can be as large as whole head count of India, as it will be very time consuming, money involvement. So to overcome this, Subset known as **Sample** is selected from Population. A **sample** is a portion of the whole and, if properly taken, is representative of the whole population.

There are many methods for selection of Sample, that we will discuss in another article.

Now, coming to **Inferential Statistics**. If a researcher gathers data from a sample and uses the statistics generated to reach conclusions about the population from which the sample was taken, the statistics is called as inferential statistics. For example, in pharmaceutical research, Some new drugs are expensive to produce, and therefore tests must be limited to small samples of patients. Utilizing inferential statistics, researchers can design experiments with small randomly selected samples of patients and attempt to reach conclusions and make inferences about the population.