I am working on my master thesis at the moment and planned on running the statistics with SigmaPlot. However, after spending some time with my data I came to the conclusion that SigmaPlot might not be fit for my problem (I may be mistaken) so I started my first attempts in R, which did not exactly make it easier.
The plan was to run a simple TWO-WAY-ANOVA on my data which results from 3 different proteins and 8 different treatments on those, so my two factors are proteins and treatments. I tested for normality using both
> ks.test(time, "norm", mean=mean(time), sd=sqrt(var(time)))
In both cases (maybe not surprising) I ended up with a non-normal distribution.
Which left me with the first questions of which test to use for equality of variances. I came up with
and the result was, that I don't have equality of variance in my data either.
I tried different data transformations (log, center, standardization), all of which did not solve my problems with the variances.
Now I am at a loss, how to conduct the ANOVA for testing which proteins and which treatments differ significantly from each other. I found something about a Kruskal-Walis-Test, but only for one factor (?). I also found things about ranking or randamization, but not yet how to implement those techniques in R.
Does anyone have a suggestion what I should do?
Edit: thank you for your answers, I am a little overwhelmed by the reading (it just seems getting more and more instead of less), but I will of course keep going.
Here an example of my data, as suggested (I am very sorry for the format, I couldn't figure out another solution or place to put a file. I am still new to this all.):
protein treatment time A con 2329.0 A HY 1072.0 A CL1 4435.0 A CL2 2971.0 A CL1-HY sim 823.5 A CL2-HY sim 491.5 A CL1+HY mix 2510.5 A CL2+HY mix 2484.5 A con 2454.0 A HY 1180.5 A CL1 3249.7 A CL2 2106.7 A CL1-HY sim 993.0 A CL2-HY sim 817.5 A CL1+HY mix 1981.0 A CL2+HY mix 2687.5 B con 1482.0 B HY 2084.7 B CL1 1498.0 B CL2 1258.5 B CL1-HY sim 1795.7 B CL2-HY sim 1804.5 B CL1+HY mix 1633.0 B CL2+HY mix 1416.3 B con 1339.0 B HY 2119.0 B CL1 1093.3 B CL2 1026.5 B CL1-HY sim 2315.5 B CL2-HY sim 2048.5 B CL1+HY mix 1465.0 B CL2+HY mix 2334.5 C con 1614.8 C HY 1525.5 C CL1 426.3 C CL2 1192.0 C CL1-HY sim 1546.0 C CL2-HY sim 874.5 C CL1+HY mix 1386.0 C CL2+HY mix 364.5 C con 1907.5 C HY 1152.5 C CL1 639.7 C CL2 1306.5 C CL1-HY sim 1515.0 C CL2-HY sim 1251.0 C CL1+HY mix 1350.5 C CL2+HY mix 1230.5