Statistics is a branch of applied mathematics and has become an essential part of data science. We live surrounded by data—but what exactly are data? Quite simply, they are observations. Some can be measured; these are known as quantitative data: height, weight, air temperature, time spent using a phone, an employee's income, and so on. When observations cannot be measured, they are known as qualitative data: favorite color, usual means of transportation, whether or not someone has diabetes, and so on.
Extracting useful information -------------------------------
Using mathematical methods and tools, statistics therefore allows us to extract useful information from data. On the one hand, we want to describe and summarize the information provided to us. This is known as descriptive statistics, and we encounter it every day. For example, a student's grades can be averaged to produce a report card. In video games, we often come across "win rates": the proportion of games won out of all those played. On the other hand, we may want to extrapolate results observed in a sample of individuals to a larger group. Returning to the school example, we could use the grades of students in a ninth-grade class to draw conclusions about the academic level of all ninth-grade students in France. This is inferential statistics.
In the age of data—and even massive datasets (big data in English)—statistics has numerous applications. Many problems involve adapting our actions in response to observations of the real world. The scientific approach itself generally requires experimental data to be collected and an assessment made of whether the observations are due to chance or to a particular phenomenon. With this in mind, let's explore some of the fields in which statisticians work.
Polls and surveys --------------------