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This is a very open ended question. Suppose I have two sets of data samples of the same form, say [item, rating]. Rating is a value on the interval [0,100] and item is a unique identifier given to a particular item. I would like to compare these two sets of data samples and determine whether the null hypothesis holds.

One caveat though. I can't look at the rating distribution. This because I have literally thousands of groups that I would like to compare and it would be too time consuming to determine the rating distribution (normal, bimodal, etc) of each group. Therefore groups that I may be comparing may have different distributions.

The naive approach would be to assume that each distribution is normal and to use something like students t test to compare groups. This is what I have been doing but I would like something more robust. Therefore how might one determine how similar/different two groups are when the two groups may have different non-normal distributions (the number of elements in the two groups may be different as well)?

edit: The item really doesn't matter. What matters is the ratings for each group.

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  • $\begingroup$ "I would like to compare these two sets of data samples and determine whether the null hypothesis holds." -- but you haven't stated one, nor alternatives you want power against. What is it you want to test for? $\endgroup$
    – Glen_b
    Apr 1 '14 at 7:57
  • $\begingroup$ the null hypothesis in this case is that there is no relationship between the two data sets. In other words they are different. Difference and similarity are whether or not the two groups were sampled from the same population. If they were, and the two groups are large enough, it is a fair assumption that the two groups should have similar means, stds, etc. $\endgroup$
    – HXSP1947
    Apr 1 '14 at 17:57
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null hypothesis in this case is that [...] they are different

-- That's not how null hypotheses work. You need something you can calculate the distribution of a test statistic under; generally that's no effect/no difference (whence, "null").

similarity ... whether or not the two groups were sampled from the same population

Your definition of 'similarity' ("from the same population") is a suitable null, fortunately.

So if the null is the population distributions are identical and the alternative is that they differ in some way, you're after a general test for distributional differences -- something that would pick up a difference in location, or spread, or shape.

This would be something like a two-sample Kolmogorov-Smirnov test. There are other possibilities, but that's the most commonly used one. If there are particular kinds of alternatives you especially want power against, there may be a more suitable choice.

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I would like to compare these two sets of data samples and determine whether the null hypothesis holds...Therefore how might one determine how similar/different two groups are...

These are two different things. It is not clear to me that you should be using hypothesis testing at all if you already know the distributions are different. See: Testing for significance between means, having one normal distributed sample and one non normal distributed

You will need to come up with a definition for "similar/different" suitable to your purpose since it sounds like you already know the groups are different.

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  • $\begingroup$ Notice "Therefore groups that I may be comparing may have different distributions". I don't know if the distributions are different or not. That is the point. Between the two groups I may have two normal distributions, one non normal and one normal, or both non normal. $\endgroup$
    – HXSP1947
    Apr 1 '14 at 18:00
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On average how many observations per item?

I agree with your naive approach and wanting something more robust. There are many robust nonparametric tests to compare two samples, such as permutation tests or Wilcoxon Rank Sum Tests. You can compare these to the results of the two-sample t-tests and look more closely at the discrepancies.

Obviously you'll want to automate this to do all variables at once using one function/command, which it sounds like you've already accomplished.

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