Nonparametric tests are experiments that do not require the underlying population for assumptions. It does not rely on data referring to any particular parametric group of probability distributions. Nonparametric methods are also called distribution-free tests since they do not have any underlying population.
Nonparametric Tests/ Statistics are Helpful when
- Inferences must be made on categorical or ordinal data
- The assumption of normality is not appropriate
- The sample size is small
Advantages of NonParametric Methods
- Easy application (does not even need a calculator in many cases)
- It can serve as a quick check to determine whether or not further analysis is required
- Many assumptions concerning the population of the data source can be relaxed
- Can be used to test categorical (yes/ no) data
- Can be used to test ordinal (1, 2, 3) data
Disadvantages of NonParametric Methods
- Nonparametric procedures are less efficient than parametric procedures. It means that nonparametric tests require a larger sample size to have the same probability of a type-I error as the equivalent parametric procedure.
- Nonparametric procedures often discard helpful information. That is, the magnitudes of the actual data values are lost. As a result, nonparametric procedures are typically less powerful.
That is they produce conclusions that have a higher probability of being incorrect. Examples of widely used Parametric Tests: include the paired and unpaired t-test, Pearson’s product-moment correlation, Analysis of Variance (ANOVA), and multiple regression.
Note: Do not use nonparametric procedures if parametric procedures can be used.
Some widely used Non-Parametric Tests are:
- Sign Test
- Runs Test
- Wilcoxon Signed Rank Test
- Wilcoxon Rank Sum Test
- Spearman’s Rank Correlation
- Kruskal Wallis Test
- Chi-Square Goodness of Fit Test
Nonparametric tests are crucial tools in statistics because they offer valuable analysis even when the data doesn’t meet the strict assumptions of parametric tests. NonParametric tests provide a valuable alternative for researchers who encounter data that doesn’t fit the mold of parametric tests. They ensure that valuable insights can still be extracted from the data without compromising the reliability of the analysis.
However, it is important to remember that nonparametric tests can sometimes be less powerful than the related parametric tests. This means NonParametric Tests might be less likely to detect a true effect, especially with smaller datasets.
In summary, nonparametric tests are valuable because these kind of tests offer flexibility in terms of data assumptions and data types. They are particularly useful for small samples, skewed data, and situations where data normality is uncertain. These tests also ensure researchers to draw statistically sound conclusions from a wider range of data types and situations. But, it is always a good practice to consider both parametric and non-parametric approaches when appropriate.
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