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In statistics, both parametric and nonparametric tests are used to make inferences about populations based on sample data, but they are founded on distinct assumptions and employed in different circumstances (Hoskin, n.d.).
According to Hoskin, parametric tests are tests that make specific assertions about the distribution of the population from which the sample is drawn (Hoskin, n.d.). When these assumptions are met, parametric tests are frequently more powerful (i.e., they can detect smaller effects with a reduced sample size).
Nonparametric tests, on the other hand, do not rely on assumptions regarding the distribution of the population (Hoskin, n.d.). They are utilized when data does not satisfy the assumptions of parametric tests, or when ordinal or nominal data is to be analyzed. Nonparametric examinations are also known as distribution-free assessments.
Nonparametric tests are deemed more robust because they make fewer assumptions, but they are frequently less effective when the data conforms to the parametric tests' assumptions. When dealing with small sample sizes, asymmetrical data, or data with outliers, they can be especially useful.
The choice between parametric and nonparametric tests is determined by the nature of the data and the possibility of meeting the parametric tests' assumptions (Hoskin, n.d.). If the assumptions are met, parametric tests are often preferred over nonparametric tests due to their higher sensitivity. When the assumptions are violated, however, nonparametric tests provide a more appropriate alternative.