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In this paper, we propose a method for measuring the similarity low sampletabular data with synthetically generated data with a larger number of samplesthan original. This process is also known as data augmentation. Butsignificance levels obtained from non-parametric tests are suspect when samplesize is small. Our method uses a combination of geometry, topology and robuststatistics for hypothesis testing in order to compare the validity of generateddata. We also compare the results with common global metric methods availablein the literature for large sample size data.

Evaluating Synthetically Generated Data from Small Sample Sizes: An Experimental Study