The field of statistical testing and fairness assessment is moving towards more robust and nuanced methods for evaluating complex data relationships. Researchers are developing innovative approaches to address the challenges of discretization, demographic bias, and intersectional analysis. A key direction is the development of sample-efficient tests that can establish independence relationships between latent continuous variables, as well as new metrics and frameworks for assessing fairness and bias in algorithmic decision-making systems. These advances have the potential to improve the accuracy and reliability of statistical conclusions, particularly in applications where subtle disparities in distribution tails can have significant impacts. Notable papers include:

• A sample-efficient conditional independence test that addresses the limitations of binarization processes,
• A size-adaptive hypothesis-testing framework for fairness assessment that provides interpretable and statistically rigorous decisions under varying degrees of data availability and intersectionality,
• A novel null model for assessing data mining results that preserves the bipartite joint degree matrix and caterpillar structures of the observed dataset.