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This work proposes a method to evaluate synthetic tabular data generated toaugment small sample datasets. While data augmentation techniques can increasesample counts for machine learning applications, traditional validationapproaches fail when applied to extremely limited sample sizes. Our experimentsacross four datasets reveal significant inconsistencies between global metricsand topological measures, with statistical tests producing unreliablesignificance values due to insufficient sample sizes. We demonstrate thatcommon metrics like propensity scoring and MMD often suggest similarity wherefundamental topological differences exist. Our proposed normalized Bottleneckdistance based metric provides complementary insights but suffers from highvariability across experimental runs and occasional values exceedingtheoretical bounds, showing inherent instability in topological approaches forvery small datasets. These findings highlight the critical need formulti-faceted evaluation methodologies when validating synthetic data generatedfrom limited samples, as no single metric reliably captures both distributionaland structural similarity.

Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets