Suppose two time series (of light flux). The goal is to determine whether the series are from the same distribution. It is usual to use the Kolmogorov-Smirnov (KS) test in this situation.
However, the times intervals are not necessarily even between the measurements, as some data may be missing. Is it correct to deduce that the test must then be on two-dimensional arrays of (time, flux)?
However, because of Why can't one generalize the Kolmogorov-Smirnov test to 2 or more dimensions?, one would use KS for two-dimensional arrays. So Beware the Kolmogorov-Smirnov test! suggests the Anderson-Darling (AD) test with bootstrap.