My Math Forum Comparing Distributions of Datasets

 April 5th, 2019, 01:21 AM #1 Newbie   Joined: Apr 2019 From: London Posts: 1 Thanks: 0 Comparing Distributions of Datasets I've been asked to find a method to best compare the distribution of a number datasets that have small sample sizes. Bonus points for a solution/result that is in a scale of 0-1, i.e. a distribution approaching 1 is bordering on perfectly unequal and a distribution approaching 0 is bordering on perfectly equal. Some examples within this dataset include: Sample A: [10,1] Sample B: [10,1,1] Sample C: [4,4,3,2,2] In other words, the method used should show A to have a distribution close close to 1 (almost perfectly unevenly distributed), B to be close to 1 but further away from 1 than A's distribution, and C to be closer to 0 (quite an equal distribution). I first thought of the Gini coefficient, which is precisely about distribution and gives values between 0-1. However it seems the Gini has a 'small-sample bias' that limits its use here, where each of the datapoints have between 1 and c.10 values. I then considered the coefficient of variance, however given results can go higher than 1 this also isn't well suited to this problem. Any pointers would be greatly appreciated!
 April 5th, 2019, 01:58 AM #2 Senior Member   Joined: Oct 2009 Posts: 772 Thanks: 279 What about entropy?

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