# Can I use Kolmogorov-Smirnov to compare two empirical distributions?

Is it OK to use the Kolmogorov-Smirnov goodness-of-fit test to compare two empirical distributions to determine whether they appear to have come from the same underlying distribution, rather than to compare one empirical distribution to a pre-specified reference distribution?

Let me try asking this another way. I collect N samples from some distribution at one location. I collect M samples at another location. The data is continuous (each sample is a real number between 0 and 10, say) but not normally distributed. I want to test whether these N+M samples all come from the same underlying distribution. Is it reasonable to use the Kolmogorov-Smirnov test for this purpose?

In particular, I could compute the empirical distribution $F_0$ from the $N$ samples, and the empirical distribution $F_1$ from the $M$ samples. Then, I could compute the Kolmogorov-Smirnov test statistic to measure the distance between $F_0$ and $F_1$: i.e., compute $D = \sup_x |F_0(x) - F_1(x)|$, and use $D$ as my test statistic as in the Kolmogorov-Smirnov test for goodness of fit. Is this a reasonable approach?

(I read elsewhere that the Kolmogorov-Smirnov test for goodness of fit is not valid for discrete distributions, but I admit I don't understand what this means or why it might be true. Does that means my proposed approach is a bad one?)

Or, do you recommend something else instead?

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In R, you can use the ks.test, which computes exact $p$-values for small sample sizes.