Parametric and non-parametric data

MHB
Thread starter Suvadip
Start date Mar 5, 2013
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Data Parametric

In summary, parametric data follows a normal distribution and can be analyzed using traditional statistical methods, while non-parametric data does not follow a normal distribution and requires alternative methods. To determine if data is parametric or non-parametric, one can visually inspect its distribution or use statistical tests. The advantages of using parametric tests include greater statistical power, but they also have stricter assumptions. Non-parametric tests have fewer assumptions but less statistical power. One should use parametric tests when data is normally distributed and meets assumptions, and non-parametric tests when it does not. It may be possible to transform non-parametric data to make it parametric, but this should only be done after consulting with a statistician or thorough research.

Mar 5, 2013

Suvadip

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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Mar 5, 2013

MarkFL

Gold Member

MHB

I would suggest that you type out your attempt so our helpers can perhaps give you some guidance.

Related to Parametric and non-parametric data

1. What is the difference between parametric and non-parametric data?

Parametric data refers to data that follows a normal distribution and can be analyzed using traditional statistical methods such as t-tests and ANOVAs. Non-parametric data, on the other hand, does not follow a normal distribution and requires alternative statistical methods such as Mann-Whitney U tests and Kruskal-Wallis tests.

2. How do I determine if my data is parametric or non-parametric?

The best way to determine if your data is parametric or non-parametric is to plot it on a histogram and visually inspect its distribution. Additionally, you can use statistical tests such as the Shapiro-Wilk test or the Kolmogorov-Smirnov test to formally test for normality.

3. What are the advantages and disadvantages of using parametric and non-parametric tests?

The main advantage of using parametric tests is their greater statistical power, meaning they are more likely to detect a significant difference if one exists. However, they also have stricter assumptions that must be met in order to produce accurate results. Non-parametric tests, on the other hand, have fewer assumptions and can be used with non-normal data, but they have less statistical power.

4. When should I use parametric tests versus non-parametric tests?

You should use parametric tests when your data is normally distributed and meets the assumptions of the test you are using. If your data does not meet these assumptions, you should use non-parametric tests. It is also important to consider the type of data you have and the research question you are trying to answer when deciding which type of test to use.

5. Can I transform non-parametric data to make it parametric?

In some cases, it may be possible to transform non-parametric data to make it more normally distributed. However, this should only be done if it is appropriate for the type of data you have and the research question you are trying to answer. It is important to consult with a statistician or conduct thorough research before attempting to transform your data.