kstest2
              Two-sample Kolmogorov-Smirnov goodness-of-fit hypothesis test.
 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.
 h = kstest2 (x1, x2, name, value)
 returns a test decision for a two-sample Kolmogorov-Smirnov test with
 additional options specified by one or more name-value pair arguments as
 shown below.
| "alpha" | A value alpha between 0 and 1 specifying the significance level. Default is 0.05 for 5% significance. | 
| "tail" | A string indicating the type of test: | 
| "unequal" | "F(X1) not equal to F(X2)" (two-sided) [Default] | |
| "larger" | "F(X1) > F(X2)" (one-sided) | |
| "smaller" | "F(X1) < F(X2)" (one-sided) | 
 The two-sided test uses the maximum absolute difference between the cdfs of
 the distributions of the two data vectors.  The test statistic is
 D* = max(|F1(x) - F2(x)|), where F1(x) is the proportion of x1
 values less or equal to x and F2(x) is the proportion of x2 values less
 than or equal to x.  The one-sided test uses the actual value of the
 difference between the cdfs of the distributions of the two data vectors
 rather than the absolute value. The test statistic is
 D* = max(F1(x) - F2(x)) or D* = max(F2(x) - F1(x)) for
 tail = "larger" or "smaller", respectively.
 [h, p] = kstest2 (…) also returns the
 asymptotic p-value p.
 [h, p, ks2stat] = kstest2 (…) also returns
 the Kolmogorov-Smirnov test statistic ks2stat defined above for the
 test type indicated by tail.
Source Code: kstest2