The main issue is that the original distributions are the same, you shift the two samples *by different amounts* (about 0.01 SD), and you have a large (n=1000) sample size. Thus the new distributions are not the same.
This is a problem with testing for equality of distributions. With large samples, even a small deviation is significant. Chris -----Original Message----- From: Monnand [mailto:monn...@gmail.com] Sent: Sunday, January 11, 2015 10:13 PM To: r-help@r-project.org Subject: [R] two-sample KS test: data becomes significantly different after normalization Hi all, This question is sort of related to R (I'm not sure if I used an R function correctly), but also related to stats in general. I'm sorry if this is considered as off-topic. I'm currently working on a data set with two sets of samples. The csv file of the data could be found here: http://pastebin.com/200v10py I would like to use KS test to see if these two sets of samples are from different distributions. I ran the following R script: # read data from the file > data = read.csv('data.csv') > ks.test(data[[1]], data[[2]]) Two-sample Kolmogorov-Smirnov test data: data[[1]] and data[[2]] D = 0.025, p-value = 0.9132 alternative hypothesis: two-sided The KS test shows that these two samples are very similar. (In fact, they should come from same distribution.) However, due to some reasons, instead of the raw values, the actual data that I will get will be normalized (zero mean, unit variance). So I tried to normalize the raw data I have and run the KS test again: > ks.test(scale(data[[1]]), scale(data[[2]])) Two-sample Kolmogorov-Smirnov test data: scale(data[[1]]) and scale(data[[2]]) D = 0.3273, p-value < 2.2e-16 alternative hypothesis: two-sided The p-value becomes almost zero after normalization indicating these two samples are significantly different (from different distributions). My question is: How the normalization could make two similar samples becomes different from each other? I can see that if two samples are different, then normalization could make them similar. However, if two sets of data are similar, then intuitively, applying same operation onto them should make them still similar, at least not different from each other too much. I did some further analysis about the data. I also tried to normalize the data into [0,1] range (using the formula (x-min(x))/(max(x)-min(x))), but same thing happened. At first, I thought it might be outliers caused this problem (I can see that an outlier may cause this problem if I normalize the data into [0,1] range.) I deleted all data whose abs value is larger than 4 standard deviation. But it still didn't help. Plus, I even plotted the eCDFs, they *really* look the same to me even after normalization. Anything wrong with my usage of the R function? Since the data contains ties, I also tried ks.boot ( http://sekhon.berkeley.edu/matching/ks.boot.html ), but I got the same result. Could anyone help me to explain why it happened? Also, any suggestion about the hypothesis testing on normalized data? (The data I have right now is simulated data. In real world, I cannot get raw data, but only normalized one.) Regards, -Monnand [[alternative HTML version deleted]] ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
