MHB Parametric and non-parametric data

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Parametric data assumes a normal distribution and is analyzed using statistical methods that rely on this assumption, such as t-tests and ANOVA. Non-parametric data does not assume a specific distribution and is analyzed using methods like the Mann-Whitney U test and Kruskal-Wallis test. The choice between parametric and non-parametric analysis depends on the data characteristics, including sample size and distribution. Parametric tests are generally more powerful when the assumptions are met, while non-parametric tests are more flexible for non-normal data. Understanding these differences is crucial for accurate data analysis in research.
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I need the answer of the following question in 500 words. It was set in a university exam. But no where I found the straight forward answer. Please help

Question: Differentiate between Parametric and non-parametric data. How these data are analysed? (Word limit 500)
 
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I would suggest that you type out your attempt so our helpers can perhaps give you some guidance.
 

Thread 'Hypothesis testing: Defining H0, HA hypotheses so that ( H_A)_A' makes sense'

The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...
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