The classification of data as a frequency distribution has an inherent shortcoming. While it summarises raw data making it concise and comprehensible it does not show the datails that are found in raw data. There is loss of information in classifying raw data. Once the data are grouped into classes, an individual observation has no significance in further statistical calculation. This is so because when the data are grouped into classes an individual observation has no significance in further calculation. This is so because when data are grouped as 'classes' all values say in a given class 0-10 are grouped with a frequency of say 6, but not their actual values. All values in this class are assumed to be equal to the middle value of the class interval (i.e., 5).
What do you understand by the 'loss of information' in classified data? Explain with the help of an example.

The data collected from primary and secondary sources are raw or unclassified. Once the data are collected the next step is to classify them for further statistical analysis. 'Classification' brings order to raw data which can be classified through the frequency distribution in a comprehensive manner. Once you know the techniques of classification, you can easily construct a frequency distribution, both for the continuous and discrete variable. A continuous variable can take any numerical value, e.g., height, weight etc. whereas a discrete variable can take only certain value, e.g., number of children, number of chairs etc.
(a) Explain how does classification of data brings order to raw data.
(b) Give an example each of a discrete variable and a continuous variable.