Comparing normal with non-normal I have two sets of data for setup time of a machine, one is setup time when fixture is running alone, another is when another fixture is running alongside. 
For fixture running alone the data I gathered is not normal although there are only 12 data points.
For parallel running fixture the setup is normally distributed with p0.089 and sample size 10
I wanted to test the null hypothesis that there is no difference in setup time for these two scenarios, would comparing the median of these two data the right way to do it or not.
I understand the sample size is too low, but this experiment is for a product where you do not get many data points. they wanted just an estimate to start working, so all I wanted to do is atleast follow the right way of doing it.
Thanks
 A: With so few data points, I wouldn't get caught up in complicated formulas, test statistics and P-values. Instead, I'd keep it simple by just asking a few questions:


*

*What is the average setup time for the two alternatives? Does running standalone result in a faster/slower time than running concurrently with another machine?

*What is the maximum setup time? The average may not be very useful if, in the worst case, one alternative takes twice as long as the other.

*Similarly, what is the standard deviation or interquartile range of setup times? The idea is to see how much variability or volatility there is in setup times. If one alternative is slightly faster on average, but could sometimes be much slower, that's important to know.
The objective is to arrive at a better qualitative understanding of how setup time can vary, and from that, decide how to proceed. With such a small dataset, any quantitative test you could perform is unlikely to tell you anything you couldn't figure out for yourself.
A: If you have only 10 data points and your goal is to know whether the two sets of samples come from the same distribution, I would not even check for normality.
As I see it, your options are:


*

*Nonparametric test for equal medians (e.g., Wilcoxon sum rank test, see @Glen_b comments though)

*Nonparameteric test for equal distributions (e.g., Kolmogorov-Smirnov)


Each of the tests above assumes something different on the target distribution, so you should see what you is logical to assume in your case. 
Edited - I suggested the wrong tests before
