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This covers all the topics for data mining
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Basic principles of design of experiments: The first principle of experimental design is control. We just illustrate the simplest form of control, that is, the comparison of two or more treatments (other forms of control will be discussed below). The purpose of comparing treatments is to prevent the effect of the explanatory variables (the effect of the new fever reducing medication in our examples) being confounded with the placebo effect and other lurking variables. The second principle of experimental design is randomization. Notice that the patients are assigned to either the Drug X groups or the placebo group through the use of random chance (conceptually, think drawing names from a hat). The goal of randomization is to produce treatment groups that are similar (except for chance variation) before the treatments begin. The third principle of experimental design is repetition, which refers to the practice of applying the treatments to many experimental units. The goal of repetition is to reduce the role of chance variation on the results of the experiment. For example, if each treatment group has only one patient, the results would depend too much on which group gets lucky and is assigned a patient that is less sick (e.g. with milder fever conditions). If we assign many patients to each group, it will be unlikely that all patients in the Drug X groups will be less sick.