# Statistical test to check if sampling was not uniform

Given a set of real numbers $$X$$ and a subset $$Y \subset X$$, I would like to demonstrate that $$Y$$ is not a result of a uniform sampling from $$X$$.

One solution that I thought recall this, that is:

1. Perform a uniform random sampling from $$X$$ to produce a subset $$X_{unif} \subset X$$ such that $$|X_{unif}| = |Y|$$.
2. Perform a Kolgomorov-Smirnov test on $$X_{unif}$$ and $$Y$$ to assess that they come from two different distributions.

By the way I think that since I have the original set $$X$$ is somewhat useless/wrong to perform an additional random sampling.

Do anyone know something that could solve this problem?

• in my opinion probability to sample a subset $Y_0$ is equal to probability of sampling any other set $Y_i$. In general you can not prove that this particular subset $Y$ was selected non-randomly - unless you have a prior knowledge of what type of bias was used during subsampling. If you have such knowledge - construct test statistic based on this knowledge, take 1000 subsamples of size |Y| and check how many test statistics from these 1000 subsamples exceed (or equal) to yours. This will be your p-value. – German Demidov Apr 10 at 13:18
• I am not sure to have understood, you are proposing to use the solution I show in the question? – BalrogOfMoria Apr 10 at 13:25
• If you change "produce a subset" to "produce subsets" - I would say yes, and Kolmogorov-Smirnov test looks like terribly bad choice if you want to use p-values from it - but even if you use just W I doubt if it is a good choice of statistic especially if |Y| is small and |X| is quite complex. – German Demidov Apr 10 at 13:31
• Why not just perform a K-S test to compare $Y$ directly to the uniform distribution on $X$? There's no need to construct a sample from $X$ in order to analyze your data! – whuber Apr 10 at 13:32
• @German I would just like to remark that the subtleties you were alluding to in earlier comments derive from the fact that under the null, every subset (of a given size) of $X$ has equal chances of being sampled. Thus, there's absolutely no way to distinguish this $Y$ from any other subset $Y^\prime.$ One makes progress by specifying an alternative to the null hypothesis: that is, exactly how could uniform sampling be violated? Might it consist, for instance, of unusually large or small gaps between the values of $Y$? Until the OP tells us this, it will be hard to justify any answer. – whuber Apr 10 at 14:34

I would combine your idea of the KS-test and the answer below your question, if the size of the $$X$$ and/or $$Y$$ is small:

Sample (uniformly) $$B$$ times (e.g. $$B=1000$$) a subset $$X_b$$ of $$X$$ of size $$|Y|$$ and calculate the Kolmogorov-Smirnov statistic $$D_b$$ from $$X_b$$ and $$X$$. This gives you an empirical distribution of the values $$D_b$$.

Calculate $$D^{\ast}$$ as the Kolmogorov-Smirnov statistic from $$Y$$ and $$X$$ and assess how many of your $$D_b$$, $$b=1,\ldots,B$$ are larger than or equal to your $$D^{\ast}$$, this gives you the approximate $$p$$-value: $$p=\#\lbrace D_b\geq D^{\ast} \rbrace/B$$.

If $$p$$ is smaller than a pre-defined $$\alpha$$, you can reject the null hypothesis that $$Y$$ was sampled uniformly with error $$\alpha$$.

If $$|X|$$ and $$|Y|$$ are large, you can calculate the KS-statistic $$D^{\ast}$$ directly and use tabellarized values to assess significance.

• $|X| \sim 3k$ and $|Y| \sim 6M$. Do you think that the two sizes allow to directly perform KS-test between $X$ and $Y$? – BalrogOfMoria Apr 10 at 13:47
• I guess it is the other way around, $|X|\approx6\cdot10^6$ and $|Y|\approx3\cdot10^3$? Yes, 3000 data points should be enough. – Edgar Apr 10 at 14:54
• Yes sorry, it is as you say. Thanks for the clarifications. – BalrogOfMoria Apr 10 at 14:57