# 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 :
1. Before the implementation date of the feature
2. 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?

• Welcome to Cross Validated! What do you mean by time data instead of numerical data? Do you mean that your Python code stores a datetime object instead of a raw number?
– Dave
Commented Apr 25, 2022 at 9:26
• Specifically, I measure time data for both groups: the duration of an action (in minutes). * The data type is less important What statistical significance test should I use? This is the focus of the question. Thanks Commented Apr 25, 2022 at 11:49
• You mention the Mann–Whitney–Wilcoxon rank sum test (U-test). It should work well for your data, as it doesn't assume the variables are normally distributed. So you have (at least) two options: U-test and permutation test. As an side: duration (measured in minutes, hours, etc) is a numeric variable, though obviously non-normal as duration is nonnegative and very often right skewed. Commented Apr 26, 2022 at 9:44

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.