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In practice, it is common to collect a large amount of nonnumerical and/or numerical data on a daily basis. For example, we may collect data concerning customer satisfaction, comments of employees, or perceptions of suppliers. Or we may track the number of employees in various departments of a company or check weekly production volume in units produced and sales dollars per unit of time, and so on. All the data collected, however, cannot be treated the same way as there are differences in types of data. Accordingly, statistical data can normally be divided into two major categories:

 Qualitative

 Quantitative

Each of these categories can be further subdivided into two subcategories each. The two subcategories of qualitative data are nominal and ordinal, whereas the two subcategories of quantitative data are interval and ratio. We may summarize this classification of statistical data as in Figure 2.2.1.

The classification of data as nominal, ordinal, interval, and ratio is arranged in the order of the amount of information they can provide. Nominal data provide minimum information, whereas ratio data provide maximum information.

Figure 2.2.1 Classifications of statistical data.