Parametric vs Non-Parametric Tests: Advantages and Disadvantages
Nonparametric tests and parametric tests are two types of statistical tests that are used to analyze data and make inferences about a population based on a sample. Nonparametric tests are used when the data do not follow a normal distribution or when the assumptions of parametric tests are not met. Parametric tests, on the other hand, are based on the assumptions of the normal distribution and can be more powerful if the assumptions are met. Both types of tests have their own advantages and disadvantages, and it is important to understand the differences between them in order to choose the appropriate test for your data.
What are the advantages and disadvantages of nonparametric tests?
One of the main advantages of nonparametric tests is that they do NOT require the assumptions of the normal distribution or homogeneity of variance (i.e., the variance of a variable is constant across different groups or categories.), which makes them more robust and widely applicable. Nonparametric tests are particularly useful when the sample size is small or the data are skewed or ordinal, as they are more forgiving of deviations from the assumptions of parametric tests. Nonparametric tests are also less sensitive to outliers, which can have a significant impact on the results of…
