Statistics is the branch of science which deals with the collection, presentation, and analysis of data, and making conclusions about the population on the basis of sample data. So, the core job of statistics is to serve multiple purposes, starting from data collection and data analysis to making inferences about a whole group on the basis of a small sample of data. For example, you may have the scores of different students in a statistics paper, a company collects data from its different branches to compare monthly sales, a meteorologist prepares a monthly report showing the average temperature, or a farmer calculates the average production of wheat per acre.

Statistics is mainly divided into two branches.

Descriptive Statistics

Descriptive statistics is used to describe the available data. This branch consists of finding measures of central tendency (mean, median, mode, quantiles, etc.) and measures of dispersion (variance, standard deviation, quartile deviation, etc.). Descriptive statistics help summarize data for better understanding. It’s like presenting data to someone who has no knowledge of statistics or even of the data’s behavior.

For example, the average fuel consumption of a vehicle, the average time taken to reach the university, and the most sold shoe size at a shoe store.  Moreover, it is also possible that sometimes you have datasets with the same mean, but that same mean might occur just by chance. In such situations, we calculate the variance and standard deviation to look deeper into the data and check whether there is any kind of deviation or variation.

Inferential Statistics

Inferential statistics is the branch of statistics which deals with making conclusions about a population on the basis of sample data. This branch helps to make inferences about the whole population without examining each and every unit of it.

For example, a company may survey 200 customers to estimate the satisfaction level of all its customers, a doctor may test a new medicine on a small group of patients to judge its effectiveness for the entire population, or an election agency may collect opinions from a few thousand voters to predict the overall election results. These are all examples of inferential statistics because they use sample information to draw conclusions about a larger group.