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DESCRIPTIVE STATISTICS

Descriptive statistics are tabular, graphical, and numerical methods by which essential features of a sample can be described. Although these same methods can be used to describe entire populations, they are more often applied to samples in order to capture population characteristics by inference. We will differentiate between two main types of data samples: qualitative data samples and quantitative data samples. Qualitative data arises when the characteristic being observed is not measurable. A typical case is the “success” or “failure” of a particular test. For example, to test the effect of a drug in a clinical trial setting, the experimenter may define two possible outcomes for each patient: either the drug was effective in treating the patient, or the drug was not effective. In the case of two possible outcomes, any sample of size n can be represented as a sequence of n nominal outcome x1, x2… xn that can assume either the value “success” or “failure”. By contrast, quantitative data arise when the characteristics being observed can be described by numbers. Discrete quantitative data is c