In cancer research, bootstrapping is widely employed to improve the robustness of statistical models, validate findings, and enhance predictive modeling. It is particularly beneficial in studies where sample sizes are small or when the data distribution is unknown. By generating multiple simulated samples, researchers can obtain more stable estimates of clinical outcomes and treatment effects.
