Theory:

Until now, we have dealt with data with discrete values. In other words, we have dealt with a finite number of data or data of a countable number of values.

Let us consider a train journey from Chennai to Bangalore. To know more about the kinds of passengers who travel, we are expected to prepare a data table based on their age groups.

For larger data like these, do you think discrete would be enough?

Age is not a discrete number. We are always a day older than the previous day. Hence, quantifiers like age, weight, height, volume and so on are called continuous values, and they also vary from person to person and from region to region.
 
In other words, quantifiers that keep varying are called continuous variables.
 
Varying quantifiers are depicted in the form of class intervals. Class intervals are prevalent only in a continuous set of data.
 
Class:
While depicting more extensive data, data of common tendencies could fall into a group, and those groups are called classes. A set of data can have any number of classes.
Example:
Let us consider the data of the heights of people opting for the adventure sport.
 
The different classes could be heights between \(150 cm\) to \(155 cm\), \(156 cm\) to \(160 cm\), and \(160 cm\) to \(165 cm\).
Class interval (C.I.):
Classes will always have an upper limit and a lower limit. Class interval is the difference between the upper limit and the lower limit. The upper limit is the class's highest value, and the lower limit is the class's lowest value.
\(\text{Class interval} = \text{Upper limit} - \text{Lower limit}\)
 
Here, Upper limit \(= 45\) and Lower limit \(= 32\).
 
Thus, \(\text{Class interval} = \text{Upper limit} - \text{Lower limit}\)
 
\(= 45 - 32\)
 
\(= 7\)