Differentiate parametric and non parametric statistics ?

Parametric statistics and non-parametric statistics are two different approaches to analyzing data in statistics.

Parametric statistics assume that the data follows a specific distribution, such as a normal distribution, and make inferences about the population parameters based on this assumption. These statistics rely on certain assumptions about the data, such as independence, homogeneity of variance, and linearity. Examples of parametric statistics include t-tests, analysis of variance (ANOVA), and linear regression.

Non-parametric statistics, on the other hand, do not make any assumptions about the underlying distribution of the data. These statistics are often used when the data does not meet the assumptions of parametric statistics or when the data is measured on an ordinal or nominal scale. Non-parametric statistics are based on ranks or frequencies rather than the actual values of the data. Examples of non-parametric statistics include the Mann-Whitney U test, Wilcoxon signed-rank test, and Kruskal-Wallis test.

In summary, the main difference between parametric and non-parametric statistics lies in the assumptions made about the data and the type of data they can analyze. Parametric statistics assume a specific distribution and make inferences based on this assumption, while non-parametric statistics do not make any assumptions about the distribution and are more flexible in analyzing different types of data.