I have two groups of discrete data (integers):
Group 1 Group 2 101 103 105 200 115 150 98 160 100 115 ... ...
and I need to know if they are significantly different or not. For this kind of tests I know there are useful tests such as
Wilcoxon test. However, I was told that this kind of statistically tests are for continuous data (not my case).
Then, I used a
Chi-squared test. However, assumptions are not met since there are a lot of cells with 0s. Even combining my data into bins (e. g. 1-19, 20-39, etc.), I have lots of cells with 0s. R throws this warning in this case:
Chi-squared approximation may be incorrect
I know as well there are the
Montecarlo simulation. However, it is just a simulation and is always giving the same p-value, exactly the same p-value, for all my different datasets to be compared. I don't like this idea.
Fisher test is practically impossible due to the size of my datasets. It is possible to use Fisher test if I group my data into bins of 1000, quite wide bins. However, I don't like this idea neither.
In summary, do you know how can I deal with my data?
- My data is not paired.
- Group 1 has about 30.000 observations while group 2 hardly has more than 4.000.
- Extremely skewed data. Example with two of my datasets: