Efficient estimation of linkage error in large linked datasets requires careful allocation of limited clerical review resources. We investigate the use of small stratified “pilot” samples as an initial, low-cost strategy for informing sample size calculation and allocation across strata when estimating binomial error proportions. Stratified candidate links and unlinked pairs exhibit heterogeneous error rates and variances; however, without prior variance information, optimal allocation – such as Neyman allocation – cannot be applied. We show that pilot samples of approximately nh = 30 per stratum provide sufficiently informative variance estimates at minimal cost, enabling efficient allocation of subsequent sampling effort. Using binomial absolute margin-of-error behaviour as a rough guide we calculate uncertainty for error proportions up to a maximum of p = 0.5, and use simulations to show that beyond nh ≈ 30 the marginal reduction in uncertainty grows negligible. Simulations illustrate also that these small pilots successfully differentiate high- and low-variance strata, supporting targeted sampling in the final round. When pilots are combined with Neyman allocation, the resulting stratum-level confidence intervals were shown to become more homogeneous, reducing overall population-level variance relative to proportional allocation. Empirical comparisons therefore show that, for equal total clerical review effort, Neyman allocation informed by pilots consistently yields narrower population-level confidence intervals than proportional allocation (0.85% vs 0.93% MoE). This confirms that even very small pilot samples can materially improve efficiency by enabling variance-driven sample size calculation and allocation. The method offers a practical, scalable approach for organisations conducting clerical review of linkage errors in large datasets.