The Z-score Evaluation, Benchmarking, and Ranking Algorithm (ZEBRA) is a custom tool for comparing the performance of indirect reference interval (RI) estimation methods. The tool converts laboratory test results into standardized z or zlog scores according to the distributional shape (approximately Gaussian vs. skewed), thereby mapping original measurements onto a common standardized scale determined by the RI limits. ZEBRA generates method-specific comparative box plots for visual inspection of standardized-score distributions across evaluated methods, using both real and simulated datasets and, when applicable, subsampling scenarios. Agreement between each indirect method and the comparative reference is quantified using Lin’s concordance correlation coefficient (ρ_c) with a 99.5% confidence interval, computed from paired comparisons between standardized scores derived from the comparative reference limits and those produced by each method. For ranking, the lower bound of the ρc confidence interval is categorized and mapped to a six-point Likert scale; scores are aggregated to generate an overall ranking of the evaluated indirect methods. Finally, the individual plots are assembled into a multi-panel composite figure for integrated reporting.