Small sample sizes are a pervasive limitation in experimental neuroscience and related biological fields, frequently compromising statistical power and the reliability of inference. Conventional resampling techniques, such as stratified or hierarchical bootstrapping, often fail to balance coverage accuracy with interval precision, particularly when data are skewed or violate strict exchangeability assumptions. Here, we introduce Variance-Calibrated Cross-Individual Bootstrapping (CIB-VC), a robust resampling framework designed for datasets characterized by few subjects but distinct within-subject trial structures. The method constructs synthetic individuals by recombining trials across subjects to maximize informational use, coupled with a variance calibration step that restores empirical between-subject variability. Using extensive Monte Carlo simulations across Gaussian, heavy-tailed, and lognormal distributions, we demonstrate that CIB-VC consistently achieves near-nominal 95% coverage. In contrast, stratified bootstrapping exhibits severe under-coverage under distributional skew (coverage <1\%), while hierarchical bootstrapping yields overly conservative confidence intervals. Crucially, empirical validation on behavioral tracking data from weakly electric fish (N=5) confirmed the practical utility of the method: CIB-VC produced confidence intervals approximately 23% narrower than standard hierarchical bootstrapping without introducing bias. By providing a statistically principled solution that preserves Type-I error control without sacrificing power, CIB-VC offers a practical tool for improving reproducibility in studies where increasing the number of subjects is ethically or logistically constrained.
Ismail Uyanik (Sun,) studied this question.