Stratified Bootstrap Test Package

TL;DR

The paper introduces the Stratified Bootstrap Test package, a nonparametric framework for assessing the stability and statistical significance of group-specific rankings in multivariate data, applicable to survey analysis and fairness auditing.

Contribution

It develops a novel stratified bootstrap methodology for both descriptive ranking stability assessment and formal hypothesis testing of group orderings, without relying on distributional assumptions.

Findings

Provides a nonparametric measure of ranking robustness

Enables formal testing of group ordering hypotheses

Applicable to survey, AI fairness, and response pattern analysis

Abstract

The Stratified Bootstrap Test (SBT) provides a nonparametric, resampling-based framework for assessing the stability of group-specific ranking patterns in multivariate survey or rating data. By repeatedly resampling observations and examining whether a group's top-ranked items remain among the highest-scoring categories across bootstrap samples, SBT quantifies ranking robustness through a non-containment index. In parallel, the stratified bootstrap test extends this framework to formal statistical inference by testing ordering hypotheses among population means. Through resampling within groups, the method approximates the null distribution of ranking-based test statistics without relying on distributional assumptions. Together, these techniques enable both descriptive and inferential evaluation of ranking consistency, detection of aberrant or adversarial response patterns, and rigorous comparison of groups in applications such as survey analysis, item response assessment, and fairness auditing in AI systems.