Hello Mahmooda,

As given, your query is unclear. If you could specify: (a) your research question of interest; (b) the variables involved (IV/s and DV/s) and how each is quantified; and (c) how data were collected, I think you'd most likely get a more focused recommendation.

Good luck with your work.

In my understanding, if your data has a non-normal distribution, you can opt for distribution-free tests based on your data, whether it involves continuous variables or discrete variables. The most widely used tests include chi-squared, Fisher's exact test, Wilcoxon's matched pairs, Mann-Whitney U-test, Kruskal-Wallis test, and Spearman's rank correlation

When the data does not follow a normal distribution, and you have one parameter represented by mean and standard deviation, you can use a non-parametric test such as the Mann-Whitney U test or the Kruskal-Wallis test.

The Mann-Whitney U test compares two independent groups and tests the null hypothesis that the two groups have the same distribution. It does not assume any particular distribution for the data and is a useful alternative to the two-sample t-test when the data are not normally distributed.

The Kruskal-Wallis test compares more than two independent groups and tests the null hypothesis that all groups have the same distribution. It also does not assume any particular distribution of the data.

Both tests are commonly used in research and are relatively easy to conduct using statistical software such as R or SPSS. It is important to note that non-parametric tests may have less power than parametric tests when the data are normally distributed, but they are more robust when the data do not meet normality assumptions.

I hope all of you realize that no raw data is normal.