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In research methodology for management decisions, understanding the distinction between parametric and non-parametric tests is critical for selecting the appropriate statistical technique to analyze data. Both types of tests serve the purpose of hypothesis testing but differ fundamentally in the assumptions they make about the data and the nature of the variables involved. Parametric tests are statistical tests that rely on assumptions about the underlying population distribution from which the samples are drawn. Typically, these tests assume that the data follow a normal distribution, the sample observations are independent, and the variance across groups is homogeneous. Parametric tests are generally more powerful than non-parametric tests when their assumptions are met, meaning they are more likely to detect a true effect when it exists. On the other hand, non-parametric tests do not require the data to follow a specific distribution and are often referred to as distribution-free tests. They are especially useful when dealing with ordinal data, non-normal distributions, or small sample sizes, and when the assumptions necessary for parametric tests cannot be satisfied. One of the primary differentiating factors between parametric and non-parametric tests is the type of data _______ _____ ___ ___ _____ __________ _________ ________.
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