The quick answer is: you can use the 2 sample Kolmogorov-Smirnov (KS) test, and this article will walk you through this process. Comparing Distributions. Often in statistics we need to understand if a given sample comes from a specific distribution, most commonly the Normal (or Gaussian) distribution. For this intent we have the so-called.. The following code shows how to perform a Kolmogorov-Smirnov test on this sample of 100 data values to determine if it came from a normal distribution: #perform Kolmogorov-Smirnov test ks.test(data, "pnorm") One-sample Kolmogorov-Smirnov test data: data D = 0.97725, p-value < 2.2e-16 alternative hypothesis: two-sided

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Basic Concepts. The two-sample Kolmogorov-Smirnov test is used to test whether two samples come from the same distribution. The procedure is very similar to the One-Sample Kolmogorov-Smirnov Test (see also Kolmogorov-Smirnov Test for Normality).. Suppose that the first sample has size m with an observed cumulative distribution function of F(x) and that the second sample has size n with an.. h = kstest2(x1,x2) returns a test decision for the null hypothesis that the data in vectors x1 and x2 are from the same continuous distribution, using the two-sample Kolmogorov-Smirnov test.The alternative hypothesis is that x1 and x2 are from different continuous distributions. The result h is 1 if the test rejects the null hypothesis at the 5% significance level, and 0 otherwise.