Suppose that there are **100** individuals in a certain sample, the sample size is denoted by . These **100** individuals are divided into two mutually exclusive groups on the basis of the attribute of height. Out of **100, 60** are tall and **40** are short. If ‘tall’ are denoted by and short are denoted by , we can write:

There are two groups and we say that there are two classes and and the class frequency under is **60**. It is written as , similarly the number of individuals under is written as . Thus the attributes written within the brackets show their class frequencies. In this example the sample is divided into two groups i.e.; two classes ‘tall’ and ‘short’. Dividing the data into two groups is called dichotomy which means cutting into two. In this example a single attribute ‘height’ divides the data in two groups. As only one attribute is involved, the data is called one-way classification. We can make a small table as below:

Clearly

The symbols

and

are used to denote the frequency of individuals who possess

and who do not possess

(

means not ‘

’). It may be noted that the symbol ‘

’ is not necessarily fixed for ‘tall. In some other discussion ‘short’ may be denoted by

.

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