Significance of Non-parametric test
A non-parametric test refers to statistical methods that do not assume a specific distribution for the underlying data, making them applicable for analyses where data may not conform to parametric assumptions. Tests such as the Kruskal-Wallis test and the Wilcoxon matched paired single ranked test are examples of non-parametric tests, often utilized in clinical studies, including those examining gene set data and auditory brainstem responses, particularly when dealing with small sample sizes or non-normal distributions.
Synonyms: Robustness test
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The concept of Non-parametric test in scientific sources
Non-parametric tests are statistical methods that do not rely on assumptions about data distribution, making them versatile for analyzing various datasets without the constraints of parametric testing assumptions.
From: The Malaysian Journal of Medical Sciences
(1) This is a type of test used for analysis due to the small number of patients involved in the study.[1] (2) The statistical methods used to analyze the data, due to the non-normal distribution of time intervals, used to compare groups.[2] (3) A non-parametric test is a statistical method used to analyze data that does not follow a normal distribution, such as the Kruskal-Wallis test.[3] (4) Statistical tests used when data do not meet the assumptions of normality, and were employed in the study to analyze differences between groups based on categorical predictors such as gender or education level.[4] (5) This is a statistical method used to analyze the data from the auditory brainstem response tests, which does not assume a normal distribution of the data.[5]