parametric and non-parametric methods are two different types of statistical analysis. Parametric methods rely on the assumption that the data follows a certain distribution, while non-parametric methods do not make any assumptions about the data. This can be important when analyzing data sets with outliers or skewness. In this blog post, we will take a closer look at these two types of analysis and discuss some examples.
What is Parametric?
Parametric is a type of modeling used in computer-aided design (CAD) software. With parametric modeling, you can create a 3D model by specifying its dimensions and other parameters. This allows you to change the model easily by changing the parameters, which is why parametric modeling is often used for product design. Parametric modeling is also well suited for creating complex shapes that would be difficult to model using traditional CAD methods. Parametric models are typically saved as files with the extension.PAR or.PRT.
What is Non-Parametric?
Non-Parametric methods are statistical methods used when the data are not Normally distributed. Non-Parametric methods are also called distribution-free methods as they do not depend on the underlying distribution of the data.
- Non-Parametric methods are very robust and have good power even when the data are not Normally distributed. Some of the Non-Parametric methods are the Wilcoxon rank sum test, Mann–Whitney U test, Kruskal–Wallis test, etc. Non-Parametric methods are used extensively in environmental sciences and medicine.
- In environmental sciences, Non-Parametric methods are used to test for trends in monitoring data that are not Normally distributed. In medicine, Non-Parametric methods are used to analyze clinical trial data that are often not Normally distributed. Non-Parametric methods are also used in social sciences and marketing research.
- Non-Parametric methods have many advantages over Parametric methods. Non-Parametric methods are more robust and have good power even when the data are not Normally distributed.
Non-Parametric methods are also easier to use as they do not require the assumption of Normality. Non-Parametric methods are also less likely to be affected by outliers than Parametric methods.
Difference between Parametric and Non-Parametric
Parametric and Non-Parametric are two types of statistics that are used to analyze data. Parametric statistics are based on the assumption that the data is normally distributed, while non-parametric statistics do not make this assumption.
- This means that parametric statistics are more powerful, but they are also less robust. This means that they are more likely to be affected by outliers. Non-parametric statistics are less powerful, but they are more robust and can be used with data that is not normal.
- When deciding which type of statistic to use, it is important to consider the nature of the data and the purpose of the analysis. Parametric statistics should be used when the data is Normally distributed and the purpose of the analysis is to make inferences about a population.
- Non-parametric statistics should be used when the data is Not Normally distributed or when the purpose of the analysis is to describe a population. Parametric tests are based on assumptions about the distribution of the data whereas nonparametric tests do not rely on these assumptions.
- As a result, parametric tests are more Powerful than nonparametric tests. However, this power comes at a cost: parametric tests require more stringent conditions in order for them to be applicable, which can limit their usefulness in practice. In contrast, nonparametric tests are more versatile and can be applied in a wider range of situations.
When choosing between parametric and nonparametric tests, it is important to consider both the nature of the data and the goals of the analysis. If the data meet the assumptions required for parametric tests and if inferences about a population are desired, then parametric tests should be used. If the data do not meet these assumptions or if descriptive analyses are desired, then nonparametric tests should be used instead.
Conclusion
In conclusion, parametric tests are more powerful than non-parametric tests, but they also require more assumptions to be true in order to produce valid results. Non-parametric tests are less powerful, but they are also less likely to make incorrect assumptions about the data. When choosing a statistical test, it is important to consider the type of data you are working with and which test will be most appropriate.
