Paired bootstrap resampling adapts general bootstrap resampling to compare two systems evaluated on the same set of items (e.g., the same test questions). Given $n$ paired per-item scores $(a_i, b_i)$ for systems $A$ and $B$ under the same metric, one draws $B$ resamples by sampling item indices with replacement; on each resample $b$, both systems' metrics are recomputed on exactly the same resampled indices and a paired delta $\hat{\Delta}_b = M_A^{(b)} - M_B^{(b)}$ is recorded. The empirical distribution of $\{\hat{\Delta}_b\}_{b=1}^{B}$ is used to form a percentile confidence interval for the true paired difference (e.g., the $2.5$th and $97.5$th percentiles for a 95% CI) and to derive a one-sided $p$-value as the fraction of resamples in which the delta has the opposite sign. A delta is treated as significant when its percentile CI excludes $0$. Pairing the resampled index set for both systems removes between-item variance and is the standard significance protocol for comparing two systems on a shared evaluation set in NLP.