I have a simulation where an animal is placed in a hostile environment and timed to see how long it can survive using some approach to survival. There are three approaches it can use to survive. I ran 300 simulations of the animal using each survival approach. All simulations take place in the same environment but there's some randomness so it's different each time. I time how many seconds the animal survives in each simulation. Living longer is better. My data looks like this:
Approach 1, Approach 2, Approach 2
45,79,38
48,32,24
85,108,44
... 300 rows of these
I'm unsure of everything I do after this point so let me know if I'm doing something stupid and wrong. I'm trying to find out if there's a statistical difference on lifespan using a particular approach.
I ran a Shapiro test on each of the samples and they came back with tiny p values, so I believe the data isn't normalized.
Data on rows have no relationship to each other. The random seed used for each simulation was different. As a result, I believe the data isn't paired.
Because the data is not normalized, not paired and there were more than two samples, I ran a Kruskal Wallis test which came back with a p-value of 0.048. I then moved on to a post hoc, selecting Mann Whitney. In really not sure if Mann Whitney should be used here.
I compared each survival approach with each other approach by performing the Mann Whitney test i.e. {(approach 1, approach 2), (approach 1, approach 3), (approach 2, approach 3)}. There was no finding of statistical significance between the pair (approach 2, approach 3) using a two tailed test but there was significance difference found using a one tailed test.
Problems:
- I don't know if using Mann Whitney like this makes sense.
- I don't know if I should be using a one or two tailed Mann Whitney.