I must compare two distributions of patent data: namely, they are the number of patent applications of companies before vs after an acquisition. I need to perform an hypothesis test to assess if the change in the number is significant.
The data are dependent (number before and number after) and extremely skewed (a lot of zeros and a few higher values).
It is clear that I cannot use the t-test and surfing the internet I was not able to find a test for both not normal (strongly skewed) and dependent samples.
I was thinking about the Wilcoxon Signed-Ranks Test for Paired Samples (http://www.real-statistics.com/non-parametric-tests/wilcoxon-signed-ranks-test/) but it seems the distribution should not be very skewed. May I use it anyway? If not, which one should I use then?
Thanks in advance.
EDIT: I found an answer here (Appropriateness of Wilcoxon signed rank test). I understand that skeweness is not a big issue for the Wilcoxon test, so I will use that one. However, if somebody does not agree or has some comment I would be glad to hear them. Thanks.