![]() A t-test in this case may help but would not give us what we require, namely the probability of a cure for a given value of the clinical score. However, if the input variable is continuous, say a clinical score, and the outcome is nominal, say cured or not cured, logistic regression is the required analysis. The required test is then the t-test (Table 2). For example, in a clinical trial the input variable is the type of treatment - a nominal variable - and the outcome may be some clinical measure perhaps Normally distributed. It is helpful to decide the input variables and the outcome variables. Table 1 Choice of statistical test from paired or matched observation The choice of test for matched or paired data is described in Table 1 and for independent data in Table 2. The next question is "what types of data are being measured?" The test used should be determined by the data. For a correct analysis of mixed paired and unpaired Health of the person and an analysis that considered ulcers as independent observations would be incorrect. We might have 20 subjects withģ0 ulcers but the number of independent pieces of information is 20 because the state of ulcers on each leg for one person may be influenced by the state of For example, suppose we were looking at treatment of leg ulcers, in which some people had an ulcer on each leg. One of the most common mistakes in statistical analysis is to treat correlated variables as if they were
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