t- test for non normally distributed sample I am doing a statistical test (analysis) for the following case:
As part of a product aimed at improving the quality and speed of code writing for developers,
we have implemented a new feature that should make code review faster.

*

*Checking the time for code reviews in these groups :


*

*Before the implementation date of the feature

*After the implementation date of the feature


*

*Each group represents a vector of times in minutes

*The 2 samples distribution is unKnown (abnormal)
I have tried several transformations to bring about a normal distribution, but without success.
So I used U-test

*I use Python to write
My question is:

What statistical significance test should I use when I have an abnormal distribution with time data and not numerical data?

For testing a difference between the means of both groups, a t-test (aka "Welch test" to make clear that no assumption of equal variances is made) can even be used when the data of both groups are not normally distributed: for a sufficient number of samples, the mean value is approximately normally distributed due to the Central Limit Theorem.
A non-parametric alternative is a permutation test: for a number (say $K=10000$) of times, permute the group labels in the data and compute the difference between the group means. The p-value is the proportion of permutations for which the difference is greater than the actually observed difference.
The usual caveat applies: statistical significance does not mean importance, and you should therefore also check whether the difference is large enough to be of practical relevance.
