Non-Parametric Test
Non-Parametric Test
Unlike the parametric test, a non-parametric test is independent of any distribution. The median value is essential in non-parametric tests as it is the basis of measurements. Therefore, the central tendency is the median value. Additionally, there are no assumptions made in a non-parametric test. The test is independent of any underlying assumptions as it is a distribution-free test. The nominal and ordinal levels help in finding the test values (Carrasco et al., 2017).
It is appropriate to perform a non-parametric test when all the independent variables are non-metric hence its name, non-parametric test. In terms of the probabilistic distribution, a non-parametric test has an arbitrary distribution. The knowledge of the population is also not required. Non-parametric tests have the edge over parametric tests because they analyze ranked and ordinal data, while parametric tests only analyze continuing data.
A non-parametric test is usually used in cases where the distribution is abnormal or skewed. These tests are considered flexible as they allow one to deal with variables and attributes equally well. It is appropriate to use a non-parametric test when the sample size is small and when the data is not in a normal distribution pattern. Mostly, when a parametric test is not appropriate, a non-parametric test is used. The non-parametric tests are used when a researcher would like to rank measurements test whether the distribution is weird (Lenart & Pippien, 2017).
When drawing a graphical representation of a non-parametric test, the curve appears skewed. An example of a non-parametric test is the Mann-Whitney U-test that is somehow related to the standard t-tests in that it looks at the differences that exist in groups but is used with ordinal data. Psychologists use the Mann-Whitney U-test a lot, especially when comparing how attitudes in patients correlate with behavioral patterns.
Assumptions to be met
Parametric and non-parametric tests have one thing in common: they both have assumptions that should be met. An investigator or a researcher must go through various conventions before successfully conducting the test (Mircioiu & Atkinson, 2017). In a parametric test, the assumptions are as follows:
- The researcher properly knows all the variables
- And the researcher should measure all the interest variables in intervals.
The assumptions in a non-parametric test are as follows:
- Nominal and ordinal methods are the primary strategies for calculating variables.
- No-parametric tests should be used with variables.
- There is no particular scale that is used to measure it.
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