How would you compare and contrast the parametric and non-parametric statistical approaches, and why might one approach be preferred over the other in certain research situations?

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Anonymous · Accepted Answer

Parametric means that the underlying distribution of the data is typically
normal, and assumes a specific data distribution. Parametric assumptions are
required for tests like the two sample t-test, ANOVA, and pearson's coefficient.
While non-parametric just means that the distribution of does not follow a
specific "standard" distribution, (ie a heavy tail when a test assumes normal).
Tests for these are typically Wilcox rank sum and etc

How would you compare and contrast the parametric and non-parametric statistical approaches, and why might one approach be preferred over the other in certain research situations?

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