ABSTRACT
Every study or experiment yields a set of data. Its size can range from a few measurements to many thousands of observations. A complete set of data, however, will not necessarily provide an investigator with information that can easily be interpreted. For example, Table 2.1 lists by row the first 2560 cases of acquired immunodeficiency syndrome (AIDS) reported to the Centers for Disease Control and Prevention [1]. Each individual was classified as either suffering from Kaposi’s sarcoma, designated by a 1, or not suffering from the disease, represented by a 0. (Kaposi’s sarcoma is a tumor that affects the skin, mucous membranes, and lymph nodes.) Although Table 2.1 displays the entire set of outcomes, it is extremely difficult to characterize the data. We cannot even identify the relative proportions of Os and Is. Between the raw data and the reported results of the study lies some intelligent and imaginative manipulation of the numbers, carried out using the methods of descriptive statistics.
